diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 0000000..5438cd9 --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,174 @@ +name: Docker + +# This will run when: +# - a new release is created, to make sure the right tags of the +# docker images are pushed (expects tags to be v1.8.4). +# - when new code is pushed to main/develop to push the tags +# latest and develop +# - when a pull request is created and updated to make sure the +# Dockerfile is still valid. +# To be able to push to dockerhub, this execpts the following +# secrets to be set in the project: +# - DOCKERHUB_USERNAME : username that can push to the org +# - DOCKERHUB_PASSWORD : password asscoaited with the username +on: + push: + branches: + - main + + pull_request: + +# Certain actions will only run when this is the main repo. +env: + MAIN_REPO: clowder-framework/smm-extractor + DOCKERHUB_ORG: socialmediamacroscope + +jobs: + docker: + runs-on: ubuntu-latest + permissions: + packages: write + + strategy: + fail-fast: false + matrix: + include: + - name: name_entity_recognition_extractor + FOLDER: name_entity_recognition_extractor + PLATFORM: "linux/amd64,linux/arm64" + - name: network_analysis_extractor + FOLDER: network_analysis_extractor + PLATFORM: "linux/amd64,linux/arm64" + - name: preprocessing_extractor + FOLDER: preprocessing_extractor + PLATFORM: "linux/amd64,linux/arm64" + - name: sentiment_analysis_extractor + FOLDER: sentiment_analysis_extractor + PLATFORM: "linux/amd64,linux/arm64" + - name: topic_modeling_extractor + FOLDER: topic_modeling_extractor + PLATFORM: "linux/amd64,linux/arm64" + + steps: + - uses: actions/checkout@v2 + + # set environment variables + - name: Extractor Version + run: | + # find out what the BRANCH is, in case of a PR we will use the PR- + if [ "${{ github.event.release.target_commitish }}" != "" ]; then + BRANCH="${{ github.event.release.target_commitish }}" + elif [[ $GITHUB_REF =~ pull ]]; then + BRANCH="$(echo $GITHUB_REF | sed 's#refs/pull/\([0-9]*\)/merge#PR-\1#')" + else + BRANCH=${GITHUB_REF##*/} + fi + + # should we push to dockerhub, and is there a README + DOCKERHUB_PUSH="false" + DOCKERHUB_README="false" + if [ "$BRANCH" == "main" -a "${{ github.repository }}" == "${{ env.MAIN_REPO }}" ]; then + if [ "${{ secrets.SMM_DOCKERHUB_USERNAME }}" != "" -a "${{ secrets.SMM_DOCKERHUB_PASSWORD }}" != "" ]; then + DOCKERHUB_PUSH="true" + if [ -e "${{ matrix.FOLDER }}/README.md" ]; then + DOCKERHUB_README="true" + fi + fi + fi + + # calculate the version and all tags + if [ "$BRANCH" == "main" ]; then + VERSION="$(awk '/"version":/ { print $2 }' ${{ matrix.FOLDER }}/extractor_info.json | sed 's/^.*"\([0-9\.]*\)".*$/\1/')" + tags="latest" + oldversion="" + tmpversion="${VERSION}" + while [ "${oldversion}" != "${tmpversion}" ]; do + oldversion="${tmpversion}" + tags="${tags} ${tmpversion}" + tmpversion=${tmpversion%.*} + done + else + VERSION="test" + tags="$BRANCH" + fi + + # create a list of all images to be pushed + IMAGE=${{ matrix.name }} + IMAGES="" + for tag in ${tags}; do + if [ "$DOCKERHUB_PUSH" == "true" ]; then + IMAGES="${IMAGES}${{ env.DOCKERHUB_ORG }}/${IMAGE}:${tag}," + fi + IMAGES="${IMAGES}ghcr.io/${{ github.repository_owner }}/${IMAGE}:${tag}," + done + IMAGES="${IMAGES%,*}" + + # save the results in env + echo "BRANCH=${BRANCH}" + echo "VERSION=${VERSION}" + echo "DOCKERHUB_README=${DOCKERHUB_README}" + echo "DOCKERHUB_PUSH=${DOCKERHUB_PUSH}" + echo "IMAGES=${IMAGES}" + + echo "BRANCH=${BRANCH}" >> $GITHUB_ENV + echo "VERSION=${VERSION}" >> $GITHUB_ENV + echo "DOCKERHUB_README=${DOCKERHUB_README}" >> $GITHUB_ENV + echo "DOCKERHUB_PUSH=${DOCKERHUB_PUSH}" >> $GITHUB_ENV + echo "IMAGES=${IMAGES}" >> $GITHUB_ENV + + # setup docker build + - name: Set up QEMU + uses: docker/setup-qemu-action@v2 + + - name: Set up Docker Buildx + id: buildx + uses: docker/setup-buildx-action@v2 + + - name: Inspect Builder + run: | + echo "Name: ${{ steps.buildx.outputs.name }}" + echo "Endpoint: ${{ steps.buildx.outputs.endpoint }}" + echo "Status: ${{ steps.buildx.outputs.status }}" + echo "Flags: ${{ steps.buildx.outputs.flags }}" + echo "Platforms: ${{ steps.buildx.outputs.platforms }}" + + # login to registries + - name: Login to DockerHub + if: env.DOCKERHUB_PUSH == 'true' + uses: docker/login-action@v2 + with: + username: ${{ secrets.SMM_DOCKERHUB_USERNAME }} + password: ${{ secrets.SMM_DOCKERHUB_PASSWORD }} + + - name: Login to GitHub Container Registry + uses: docker/login-action@v2 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + + # build the docker images + - name: Build and push ${{ matrix.name }} + uses: docker/build-push-action@v2 + with: + push: true + file: ${{ matrix.FOLDER }}/extractor.dockerfile + context: ${{ matrix.FOLDER }} + platforms: ${{ matrix.PLATFORM }} + cache-from: type=gha,scope=${{ matrix.name }} + cache-to: type=gha,mode=max,scope=${{ matrix.name }} + tags: ${{ env.IMAGES }} + build-args: | + VERSION=${{ env.VERSION }} + BUILDNUMBER=${{ github.run_number }} + GITSHA1=${{ github.sha }} + + # this will update the README of the dockerhub repo + - name: Docker Hub Description + if: env.DOCKERHUB_README == 'true' + uses: peter-evans/dockerhub-description@v2 + env: + DOCKERHUB_USERNAME: ${{ secrets.SMM_DOCKERHUB_USERNAME }} + DOCKERHUB_PASSWORD: ${{ secrets.SMM_DOCKERHUB_PASSWORD }} + DOCKERHUB_REPOSITORY: ${{ env.DOCKERHUB_ORG }}/${{ matrix.name }} + README_FILEPATH: ${{ matrix.FOLDER }}/README.md diff --git a/command.txt b/command.txt new file mode 100644 index 0000000..8309ac4 --- /dev/null +++ b/command.txt @@ -0,0 +1,14 @@ +docker build -f extractor.dockerfile -t socialmediamacroscope/name_entity_recognition_extractor:latest . +docker push socialmediamacroscope/name_entity_recognition_extractor:latest + +docker build -f extractor.dockerfile -t socialmediamacroscope/network_analysis_extractor:latest . +docker push socialmediamacroscope/network_analysis_extractor:latest + +docker build -f extractor.dockerfile -t socialmediamacroscope/preprocessing_extractor:latest . +docker push socialmediamacroscope/preprocessing_extractor:latest + +docker build -f extractor.dockerfile -t socialmediamacroscope/sentiment_analysis_extractor:latest . +docker push socialmediamacroscope/sentiment_analysis_extractor:latest + +docker build -f extractor.dockerfile -t socialmediamacroscope/topic_modeling_extractor:latest . +docker push socialmediamacroscope/topic_modeling_extractor:latest diff --git a/name_entity_recognition/LICENSE b/name_entity_recognition/LICENSE deleted file mode 100644 index 9cecc1d..0000000 --- a/name_entity_recognition/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/name_entity_recognition/algorithm.py b/name_entity_recognition/algorithm.py deleted file mode 100644 index 98f16f7..0000000 --- a/name_entity_recognition/algorithm.py +++ /dev/null @@ -1,93 +0,0 @@ -import pandas as pd -from run_ner import TwitterNER -from twokenize import tokenizeRawTweetText -import plot - - -def algorithm(df=None, params=None): - """ - wrapper function to put each individual algorithm inside - :param df: dataframe that contains all the input dataset - :param params: algorithm specific parameters - :return: a dictionary of { outputname: output content in memory } - """ - - output = {} - - # user specify which column to; each row is a sentence, get a list of sentences - column = params['column'] - sentences = df[df[column] != ''][column].dropna().astype('str').tolist() - - entity_list = [] - entity_freq = {} - entity_category = {} - - # extract entities in each sentence - ner = TwitterNER() - for sentence in sentences: - tokens = tokenizeRawTweetText(sentence) - raw_entities = ner.get_entities(tokens) - - entities = [] - for entry in raw_entities: - # record entities - entity = " ".join(tokens[entry[0]:entry[1]]) - category = entry[2] - entities.append((entity, category)) - - # record entity frequency - if entity not in entity_freq.keys(): - entity_freq[entity] = 1 - else: - entity_freq[entity] += 1 - - # record category - if category not in entity_category.keys(): - entity_category[category] = 1 - else: - entity_category[category] += 1 - - entity_list.append(entities) - - # extract entities in each sentence - output['entity'] = entity_list - - # plot bar chart of most frequent entities - output['freq'] = entity_freq - - output['div_freq'] = plot.plot_bar_chart(list(entity_freq.keys())[:30], - list(entity_freq.values())[:30], - "Top 30 Most Frequent Name Entities") - - # plot pie chart of entity categories - output['div_category'] = plot.plot_pie_chart(list(entity_category.keys()), - list(entity_category.values()), - "Name Entity Category Breakdowns") - - return output - - -if __name__ == '__main__': - """ - help user with no access to AWS test their model - to test just run algorithm.py: - python3 algorithm.py - """ - - # download our example dataset and place it under the same directory of this script - df = pd.read_csv('example_dataset.csv') - - # add your parameters needed by the analysis - params = { - "column": "text" - } - - # execute your algorithm - output = algorithm(df, params) - - # see if the outputs are what you desired - print(output.keys()) - print(output['entity'][:5]) - print(output['freq']) - print(output['div_freq'][:100]) - print(output['div_category'][:100]) diff --git a/name_entity_recognition/batch_function.py b/name_entity_recognition/batch_function.py deleted file mode 100644 index 63ba1ea..0000000 --- a/name_entity_recognition/batch_function.py +++ /dev/null @@ -1,58 +0,0 @@ -import dataset -import argparse -from notification import notification -from algorithm import algorithm - -if __name__ == '__main__': - - # entrance to invoke Batch - urls = {} - - # default parameters - parser = argparse.ArgumentParser(description="processing...") - parser.add_argument('--remoteReadPath', required=True) - parser.add_argument('--column', required=True) - parser.add_argument('--s3FolderName', required=True) - parser.add_argument('--uid', required=True) - parser.add_argument('--resultPath', required=True) - parser.add_argument('--email', required=True) - parser.add_argument('--sessionURL', required=True) - - # user specified parameters - parsed, unknown = parser.parse_known_args() - for arg in unknown: - if arg.startswith("--"): - parser.add_argument(arg, required=False) - - params = vars(parser.parse_args()) - - # arranging the paths - path = dataset.organize_path_lambda(params) - - # save the config file - urls['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - urls[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - urls[key] = value - - # push notification email - notification(toaddr=params['email'], case=3, filename=path['remoteSavePath'], - links=urls, sessionURL=params['sessionURL']) diff --git a/name_entity_recognition/clear_cache.sh b/name_entity_recognition/clear_cache.sh deleted file mode 100644 index 640c2ac..0000000 --- a/name_entity_recognition/clear_cache.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env bash -CACHEDIR=/tmp - -# delete file older than 4 hours -find "$CACHEDIR" -maxdepth 1 -mindepth 1 -type d -mmin +240 -print0 | - while IFS= read -d '' -r dir; do - rm -rf "$dir" - done \ No newline at end of file diff --git a/name_entity_recognition/clear_cache_cron b/name_entity_recognition/clear_cache_cron deleted file mode 100644 index 0510c51..0000000 --- a/name_entity_recognition/clear_cache_cron +++ /dev/null @@ -1 +0,0 @@ -0 * * * * /bin/bash /scripts/clear_cache.sh diff --git a/name_entity_recognition/command.txt b/name_entity_recognition/command.txt deleted file mode 100644 index 1e07de4..0000000 --- a/name_entity_recognition/command.txt +++ /dev/null @@ -1,3 +0,0 @@ -docker build -t socialmediamacroscope/name_entity_recognition:latest . -docker build -f extractor.dockerfile -t socialmediamacroscope/name_entity_recognition_extractor:latest . -docker push socialmediamacroscope/name_entity_recognition_extractor:latest diff --git a/name_entity_recognition/conlleval.py b/name_entity_recognition/conlleval.py deleted file mode 100644 index 2d8fb29..0000000 --- a/name_entity_recognition/conlleval.py +++ /dev/null @@ -1,259 +0,0 @@ -#!/usr/bin/env python - -## Original script taken from https://github.com/spyysalo/conlleval.py -## Modifications made by Shubhanshu Mishra to support notypes argument and functional api - -# Python version of the evaluation script from CoNLL'00- - -# Intentional differences: -# - accept any space as delimiter by default -# - optional file argument (default STDIN) -# - option to set boundary (-b argument) -# - LaTeX output (-l argument) not supported -# - raw tags (-r argument) not supported - -import re -import sys -from collections import defaultdict, namedtuple - -ANY_SPACE = '' - -class FormatError(Exception): - pass - -Metrics = namedtuple('Metrics', 'tp fp fn prec rec fscore') - -class EvalCounts(object): - def __init__(self): - self.correct_chunk = 0 # number of correctly identified chunks - self.correct_tags = 0 # number of correct chunk tags - self.found_correct = 0 # number of chunks in corpus - self.found_guessed = 0 # number of identified chunks - self.token_counter = 0 # token counter (ignores sentence breaks) - - # counts by type - self.t_correct_chunk = defaultdict(int) - self.t_found_correct = defaultdict(int) - self.t_found_guessed = defaultdict(int) - -def parse_args(argv): - import argparse - parser = argparse.ArgumentParser( - description='evaluate tagging results using CoNLL criteria', - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - arg = parser.add_argument - arg('-b', '--boundary', metavar='STR', default='-X-', - help='sentence boundary') - arg('-d', '--delimiter', metavar='CHAR', default=ANY_SPACE, - help='character delimiting items in input') - arg('-o', '--otag', metavar='CHAR', default='O', - help='alternative outside tag') - arg('-t', '--no-types', action='store_const', const=True, default=False, - help='evaluate without entity types') - arg('file', nargs='?', default=None) - return parser.parse_args(argv) - -def parse_tag(t): - m = re.match(r'^([^-]*)-(.*)$', t) - return m.groups() if m else (t, '') - -def evaluate(iterable, options=None): - if options is None: - options = parse_args([]) # use defaults - counts = EvalCounts() - num_features = None # number of features per line - in_correct = False # currently processed chunks is correct until now - last_correct = 'O' # previous chunk tag in corpus - last_correct_type = '' # type of previously identified chunk tag - last_guessed = 'O' # previously identified chunk tag - last_guessed_type = '' # type of previous chunk tag in corpus - new_sent=True - - for line in iterable: - line = line.rstrip('\r\n') - - if options.delimiter == ANY_SPACE: - features = line.split() - else: - features = line.split(options.delimiter)[-2:] - - if num_features is None: - num_features = len(features) - elif num_features != len(features) and len(features) != 0: - raise FormatError('unexpected number of features: %d (%d)' % - (len(features), num_features)) - - if len(features) == 0 or features[0] == options.boundary: - features = ['O', 'O'] - new_sent=True - else: - new_sent=False - if len(features) < 2: - raise FormatError('unexpected number of features in line %s' % line) - - guessed, guessed_type = parse_tag(features.pop()) - correct, correct_type = parse_tag(features.pop()) - if options.no_types: - guessed_type = '' - correct_type = '' - - if new_sent: - guessed = 'O' - - end_correct = end_of_chunk(last_correct, correct, - last_correct_type, correct_type) - end_guessed = end_of_chunk(last_guessed, guessed, - last_guessed_type, guessed_type) - start_correct = start_of_chunk(last_correct, correct, - last_correct_type, correct_type) - start_guessed = start_of_chunk(last_guessed, guessed, - last_guessed_type, guessed_type) - - if in_correct: - if (end_correct and end_guessed and - last_guessed_type == last_correct_type): - in_correct = False - counts.correct_chunk += 1 - counts.t_correct_chunk[last_correct_type] += 1 - elif (end_correct != end_guessed or guessed_type != correct_type): - in_correct = False - - if start_correct and start_guessed and guessed_type == correct_type: - in_correct = True - - if start_correct: - counts.found_correct += 1 - counts.t_found_correct[correct_type] += 1 - if start_guessed: - counts.found_guessed += 1 - counts.t_found_guessed[guessed_type] += 1 - if not new_sent: - if correct == guessed and guessed_type == correct_type: - counts.correct_tags += 1 - counts.token_counter += 1 - - last_guessed = guessed - last_correct = correct - last_guessed_type = guessed_type - last_correct_type = correct_type - - if in_correct: - counts.correct_chunk += 1 - counts.t_correct_chunk[last_correct_type] += 1 - - return counts - -def uniq(iterable): - seen = set() - return [i for i in iterable if not (i in seen or seen.add(i))] - -def calculate_metrics(correct, guessed, total): - tp, fp, fn = correct, guessed-correct, total-correct - p = 0 if tp + fp == 0 else 1.*tp / (tp + fp) - r = 0 if tp + fn == 0 else 1.*tp / (tp + fn) - f = 0 if p + r == 0 else 2 * p * r / (p + r) - return Metrics(tp, fp, fn, p, r, f) - -def metrics(counts): - c = counts - overall = calculate_metrics( - c.correct_chunk, c.found_guessed, c.found_correct - ) - by_type = {} - for t in uniq(c.t_found_correct.keys() + c.t_found_guessed.keys()): - by_type[t] = calculate_metrics( - c.t_correct_chunk[t], c.t_found_guessed[t], c.t_found_correct[t] - ) - return overall, by_type - -def report(counts, out=None): - if out is None: - out = sys.stdout - - overall, by_type = metrics(counts) - - c = counts - out.write('processed %d tokens with %d phrases; ' % - (c.token_counter, c.found_correct)) - out.write('found: %d phrases; correct: %d.\n' % - (c.found_guessed, c.correct_chunk)) - - if c.token_counter > 0: - out.write('accuracy: %6.2f%%; ' % - (100.*c.correct_tags/c.token_counter)) - out.write('precision: %6.2f%%; ' % (100.*overall.prec)) - out.write('recall: %6.2f%%; ' % (100.*overall.rec)) - out.write('FB1: %6.2f\n' % (100.*overall.fscore)) - - for i, m in sorted(by_type.items()): - out.write('%17s: ' % i) - out.write('precision: %6.2f%%; ' % (100.*m.prec)) - out.write('recall: %6.2f%%; ' % (100.*m.rec)) - out.write('FB1: %6.2f %d\n' % (100.*m.fscore, c.t_found_guessed[i])) - -def end_of_chunk(prev_tag, tag, prev_type, type_): - # check if a chunk ended between the previous and current word - # arguments: previous and current chunk tags, previous and current types - chunk_end = False - - if prev_tag == 'E': chunk_end = True - if prev_tag == 'U': chunk_end = True - - if prev_tag == 'B' and tag == 'B': chunk_end = True - if prev_tag == 'B' and tag == 'U': chunk_end = True - if prev_tag == 'B' and tag == 'O': chunk_end = True - if prev_tag == 'I' and tag == 'B': chunk_end = True - if prev_tag == 'I' and tag == 'U': chunk_end = True - if prev_tag == 'I' and tag == 'O': chunk_end = True - - if prev_tag != 'O' and prev_tag != '.' and prev_type != type_: - chunk_end = True - - # these chunks are assumed to have length 1 - if prev_tag == ']': chunk_end = True - if prev_tag == '[': chunk_end = True - - return chunk_end - -def start_of_chunk(prev_tag, tag, prev_type, type_): - # check if a chunk started between the previous and current word - # arguments: previous and current chunk tags, previous and current types - chunk_start = False - - if tag == 'B': chunk_start = True - if tag == 'U': chunk_start = True - - if prev_tag == 'E' and tag == 'E': chunk_start = True - if prev_tag == 'E' and tag == 'I': chunk_start = True - if prev_tag == 'U' and tag == 'E': chunk_start = True - if prev_tag == 'U' and tag == 'I': chunk_start = True - if prev_tag == 'O' and tag == 'E': chunk_start = True - if prev_tag == 'O' and tag == 'I': chunk_start = True - - if tag != 'O' and tag != '.' and prev_type != type_: - chunk_start = True - - # these chunks are assumed to have length 1 - if tag == '[': chunk_start = True - if tag == ']': chunk_start = True - - return chunk_start - -def evaluate_from_file(args, outstream=None): - with open(args.file, encoding="utf-8", errors="ignore") as f: - counts = evaluate(f, args) - report(counts, outstream) - -def main(argv): - args = parse_args(argv[1:]) - - if args.file is None: - counts = evaluate(sys.stdin, args) - else: - with open(args.file, encoding="utf-8", errors="ignore") as f: - counts = evaluate(f, args) - report(counts) - -if __name__ == '__main__': - sys.exit(main(sys.argv)) diff --git a/name_entity_recognition/data/50mpaths2 b/name_entity_recognition/data/50mpaths2 deleted file mode 100644 index 96aa63c..0000000 --- a/name_entity_recognition/data/50mpaths2 +++ /dev/null @@ -1,216856 +0,0 @@ -0000 2)i 40 -0000 \i 40 -0000 /i/ 40 -0000 today-i 41 -0000 nowi 41 -0000 #youever 47 -0000 ifinally 47 -0000 「i 47 -0000 -i- 49 -0000 ineva 49 -0000 »i 50 -0000 whattaya 53 -0000 iiiiiiiiii 53 -0000  56 -0000 ikinda 60 -0000 lol-i 61 -0000 iactually 64 -0000 waddya 68 -0000 #aslongasyou 69 -0000 doyou 69 -0000 ‎​i 72 -0000 i̇ 75 -0000 ï 81 -0000 #lolatgirlswho 90 -0000 #rtifyou 94 -0000 ijst 96 -0000 «i 99 -0000 •i 101 -0000 whoda 102 -0000 whadya 102 -0000 )i 106 -0000 +i 111 -0000 #yourfacemakesme 114 -0000 iiiiiiii 117 -0000 `i 117 -0000 iiiiiii 121 -0000 ialready 121 -0000 _i 121 -0000 #youmakeme 123 -0000 *i 126 -0000 |i 132 -0000 #urboyfriendever 142 -0000 wheni 151 -0000 ι 158 -0000 don'tcha 159 -0000 who'da 178 -0000 dyou 187 -0000 whaddaya 192 -0000 ionly 211 -0000 ijuss 236 -0000 ialways 241 -0000 iiiii 254 -0000 doncha 261 -0000 (i 293 -0000 d'ya 295 -0000 ı 305 -0000 #uever 347 -0000 inever 362 -0000 i-i 393 -0000 ijus 424 -0000 //i 438 -0000 istill 477 -0000 whaddya 511 -0000 d'you 527 -0000 ireally 705 -0000 dontcha 872 -0000 ijust 1446 -0000 i 17071657 -0000 -i 4254 -000100 iyou 41 -000100 #innowayshapeorform 41 -000100 (you 44 -000100 //we 50 -000100 //u 58 -000100 #menmarrywomenthat 61 -000100 /we 76 -000100 self-education 83 -000100 #realgrandmas 89 -000100 /you 90 -000100 #shoutouttogirlswho 92 -000100 #boyswho 92 -000100 i/we 95 -000100 #shoutouttotheguysthat 105 -000100 //you 106 -000100 #ilovepeoplethat 119 -000100 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154 -01101111110100 disputing 155 -01101111110100 honing 155 -01101111110100 inputting 157 -01101111110100 re-arranging 158 -01101111110100 squishing 159 -01101111110100 alienating 161 -01101111110100 visting 162 -01101111110100 envying 164 -01101111110100 resending 165 -01101111110100 ogling 166 -01101111110100 gritting 166 -01101111110100 nuking 166 -01101111110100 drugging 174 -01101111110100 refilling 174 -01101111110100 updatin 174 -01101111110100 eyeballing 176 -01101111110100 slurring 178 -01101111110100 textinq 178 -01101111110100 conceding 179 -01101111110100 dissolving 182 -01101111110100 dropin 182 -01101111110100 gauging 183 -01101111110100 yanking 183 -01101111110100 pitting 185 -01101111110100 slandering 186 -01101111110100 mimicking 186 -01101111110100 shunning 187 -01101111110100 prying 188 -01101111110100 fondling 188 -01101111110100 clenching 189 -01101111110100 wooing 190 -01101111110100 ramming 191 -01101111110100 payn 191 -01101111110100 escorting 191 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251 -01101111110100 spiking 256 -01101111110100 scamming 258 -01101111110100 luring 259 -01101111110100 critiquing 259 -01101111110100 enriching 259 -01101111110100 patrolling 259 -01101111110100 sparing 261 -01101111110100 underestimating 262 -01101111110100 infecting 267 -01101111110100 undressing 268 -01101111110100 dismissing 272 -01101111110100 applauding 273 -01101111110100 narrating 274 -01101111110100 deletin 276 -01101111110100 penetrating 277 -01101111110100 persuading 277 -01101111110100 effecting 284 -01101111110100 redirecting 286 -01101111110100 supervising 291 -01101111110100 stoping 292 -01101111110100 slaughtering 295 -01101111110100 strangling 296 -01101111110100 twiddling 301 -01101111110100 overtaking 301 -01101111110100 friending 307 -01101111110100 evacuating 313 -01101111110100 quiting 314 -01101111110100 butchering 316 -01101111110100 swiping 320 -01101111110100 ridding 323 -01101111110100 leavn 328 -01101111110100 prompting 328 -01101111110100 polluting 330 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spearheading 56 -01101111110110 bolstering 56 -01101111110110 obscuring 56 -01101111110110 negating 57 -01101111110110 devaluing 57 -01101111110110 monopolizing 57 -01101111110110 re-using 57 -01101111110110 redrawing 58 -01101111110110 falsifying 58 -01101111110110 televising 58 -01101111110110 foiling 58 -01101111110110 combatting 59 -01101111110110 reassessing 59 -01101111110110 reprogramming 60 -01101111110110 funneling 60 -01101111110110 pri's 60 -01101111110110 discarding 60 -01101111110110 dispersing 60 -01101111110110 modernizing 60 -01101111110110 fortifying 61 -01101111110110 politicizing 62 -01101111110110 inhibiting 63 -01101111110110 pocketing 63 -01101111110110 usingg 63 -01101111110110 retracing 63 -01101111110110 affording 63 -01101111110110 erecting 64 -01101111110110 contaminating 64 -01101111110110 trademarking 64 -01101111110110 expelling 64 -01101111110110 tainting 65 -01101111110110 re-creating 65 -01101111110110 nabbing 65 -01101111110110 mediating 65 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76 -1110010101 neils 76 -1110010101 hib 76 -1110010101 kron 76 -1110010101 f12 76 -1110010101 nere 76 -1110010101 skat 76 -1110010101 anan 76 -1110010101 vitoria 76 -1110010101 zk 76 -1110010101 cerro 76 -1110010101 rini 76 -1110010101 sgm 76 -1110010101 7e 76 -1110010101 bcf 76 -1110010101 nww 76 -1110010101 carita 76 -1110010101 fst 76 -1110010101 hrr 76 -1110010101 iwatch 76 -1110010101 #maquinariafestival 76 -1110010101 enso 76 -1110010101 ajo 76 -1110010101 zheng 76 -1110010101 ahjumma 76 -1110010101 #patd 77 -1110010101 psr 77 -1110010101 athan 77 -1110010101 paye 77 -1110010101 kueh 77 -1110010101 dessy 77 -1110010101 whm 77 -1110010101 buna 77 -1110010101 restaurante 77 -1110010101 energia 77 -1110010101 igs 77 -1110010101 chicha 77 -1110010101 tela 77 -1110010101 facile 77 -1110010101 notif 77 -1110010101 7800 77 -1110010101 thaty 77 -1110010101 hmd 77 -1110010101 zis 77 -1110010101 #orkut 77 -1110010101 foreach 77 -1110010101 cdf 77 -1110010101 kpd 77 -1110010101 alor 77 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89 -1110010101 4l 89 -1110010101 rht 89 -1110010101 aag 89 -1110010101 dmm 89 -1110010101 libera 89 -1110010101 yv 89 -1110010101 ipb 89 -1110010101 bbj 89 -1110010101 riya 90 -1110010101 nasional 90 -1110010101 kier 90 -1110010101 omicron 90 -1110010101 nonhle 90 -1110010101 idfc 90 -1110010101 bcr 90 -1110010101 iet 90 -1110010101 moj 90 -1110010101 #el 90 -1110010101 kela 90 -1110010101 best's 90 -1110010101 9h 90 -1110010101 fils 90 -1110010101 kuna 90 -1110010101 oob 90 -1110010101 akaka 91 -1110010101 chanell 91 -1110010101 ameer 91 -1110010101 vente 91 -1110010101 aji 91 -1110010101 fahad 91 -1110010101 gdm 91 -1110010101 truste 91 -1110010101 nega 91 -1110010101 18h 91 -1110010101 molt 91 -1110010101 dib 91 -1110010101 mutya 91 -1110010101 dbc 91 -1110010101 prj 91 -1110010101 rmv 91 -1110010101 bams 91 -1110010101 mias 91 -1110010101 psps 91 -1110010101 teow 91 -1110010101 b7 92 -1110010101 3r 92 -1110010101 4info 92 -1110010101 vam 92 -1110010101 5t 92 -1110010101 fme 92 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102 -1110010101 20° 102 -1110010101 capricho 102 -1110010101 zm 102 -1110010101 whp 102 -1110010101 nla 102 -1110010101 wau 102 -1110010101 subi 102 -1110010101 overal 102 -1110010101 zh 102 -1110010101 padme 102 -1110010101 btp 102 -1110010101 $t 103 -1110010101 sdm 103 -1110010101 duitsland 103 -1110010101 qk 103 -1110010101 nass 103 -1110010101 cherrybelle 103 -1110010101 -word 103 -1110010101 itw 103 -1110010101 zb 103 -1110010101 tct 103 -1110010101 #alex 103 -1110010101 murad 103 -1110010101 3n 103 -1110010101 masai 103 -1110010101 koma 103 -1110010101 vici 103 -1110010101 20h 103 -1110010101 shinichi 103 -1110010101 bambang 103 -1110010101 kage 103 -1110010101 edy 104 -1110010101 bte 104 -1110010101 tlm 104 -1110010101 daf 104 -1110010101 yama 104 -1110010101 dpm 104 -1110010101 otg 104 -1110010101 cnm 104 -1110010101 lipps 104 -1110010101 nataly 104 -1110010101 kudo 104 -1110010101 fio 104 -1110010101 zj 104 -1110010101 stadion 104 -1110010101 kartini 104 -1110010101 keo 104 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46 -11100101101 addit 46 -11100101101 integr 46 -11100101101 remot 46 -11100101101 groo 46 -11100101101 apartme 46 -11100101101 smoo 46 -11100101101 anywa 46 -11100101101 enr 46 -11100101101 recov 46 -11100101101 airp 46 -11100101101 confid 46 -11100101101 resour 46 -11100101101 operatio 46 -11100101101 movemen 46 -11100101101 platf 46 -11100101101 simila 46 -11100101101 plast 46 -11100101101 passio 46 -11100101101 hurri 46 -11100101101 antici 46 -11100101101 wonderf 46 -11100101101 seatt 46 -11100101101 volu 46 -11100101101 completel 46 -11100101101 docto 46 -11100101101 advic 46 -11100101101 exclusi 46 -11100101101 advant 46 -11100101101 realit 46 -11100101101 operatin 46 -11100101101 bedro 46 -11100101101 degr 46 -11100101101 #sho 46 -11100101101 stora 47 -11100101101 confiden 47 -11100101101 silen 47 -11100101101 philippi 47 -11100101101 #x2 47 -11100101101 himse 47 -11100101101 ¯\ 47 -11100101101 risi 47 -11100101101 neighb 47 -11100101101 desir 47 -11100101101 tradit 47 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physi 49 -11100101101 programmin 49 -11100101101 estab 49 -11100101101 journe 49 -11100101101 logi 49 -11100101101 regar 49 -11100101101 engineerin 50 -11100101101 avoi 50 -11100101101 diffic 50 -11100101101 categ 50 -11100101101 individua 50 -11100101101 toron 50 -11100101101 displ 50 -11100101101 comman 50 -11100101101 approac 50 -11100101101 northe 50 -11100101101 unv 50 -11100101101 unio 50 -11100101101 europea 50 -11100101101 sexu 50 -11100101101 surve 50 -11100101101 augu 50 -11100101101 wireles 50 -11100101101 especia 50 -11100101101 suspec 50 -11100101101 rej 50 -11100101101 resc 50 -11100101101 atto 50 -11100101101 tribun 50 -11100101101 abil 50 -11100101101 behav 50 -11100101101 libr 50 -11100101101 businesse 50 -11100101101 previo 50 -11100101101 apprecia 50 -11100101101 punc 50 -11100101101 submitt 50 -11100101101 electroni 51 -11100101101 exclus 51 -11100101101 smartph 51 -11100101101 guita 51 -11100101101 selli 51 -11100101101 upco 51 -11100101101 compos 51 -11100101101 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hollywoo 56 -11100101101 phoen 56 -11100101101 twil 57 -11100101101 kindl 57 -11100101101 productio 57 -11100101101 commissio 57 -11100101101 confirme 57 -11100101101 outl 57 -11100101101 baske 57 -11100101101 functio 57 -11100101101 successf 57 -11100101101 spons 57 -11100101101 certai 57 -11100101101 expla 57 -11100101101 directio 57 -11100101101 arsen 57 -11100101101 patr 57 -11100101101 affili 57 -11100101101 lyr 57 -11100101101 vehic 57 -11100101101 execu 57 -11100101101 photograp 58 -11100101101 affiliat 58 -11100101101 regu 58 -11100101101 criti 58 -11100101101 weddi 58 -11100101101 @ch 58 -11100101101 specifi 58 -11100101101 octob 58 -11100101101 repre 58 -11100101101 appar 58 -11100101101 exac 58 -11100101101 refe 58 -11100101101 stoo 58 -11100101101 bedr 58 -11100101101 president- 58 -11100101101 insti 58 -11100101101 appreci 58 -11100101101 authori 58 -11100101101 resourc 58 -11100101101 mortg 58 -11100101101 butto 58 -11100101101 seeki 58 -11100101101 arriv 58 -11100101101 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41 -11101010011100 andwae 41 -11101010011100 aaaaaaaaaaaaaaaaaah 41 -11101010011100 uugghhhh 41 -11101010011100 woooowwwww 41 -11101010011100 whoaah 41 -11101010011100 shhhhhhhhhhhhh 41 -11101010011100 damnnnnnnnnnn 41 -11101010011100 42 -11101010011100 c'monn 42 -11101010011100 aaaahhhhhhhh 42 -11101010011100 waaaaahhhh 42 -11101010011100 rrrrrrrrr 42 -11101010011100 weeeeh 42 -11101010011100 yeeeeesss 42 -11101010011100 #happybirthdaykaty 42 -11101010011100 aaannnddd 42 -11101010011100 #hartofdixie 42 -11101010011100 wowzerz 42 -11101010011100 maynnn 42 -11101010011100 wooowwwww 42 -11101010011100 ya5i 42 -11101010011100 shett 42 -11101010011100 omfggggggg 42 -11101010011100 aaaaaahhhhhhh 42 -11101010011100 omoo 42 -11101010011100 ogod 43 -11101010011100 aooow 43 -11101010011100 haaaaaaaaaaaaaaa 43 -11101010011100 #sayword 43 -11101010011100 woooooooooooooooow 43 -11101010011100 daymm 43 -11101010011100 ahhhhhhhhhhhhhhhhhhhhhhhhh 43 -11101010011100 wooohhh 43 -11101010011100 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46 -11101010011100 yeeeaaaahhh 47 -11101010011100 aaaaaaahhhh 47 -11101010011100 aaaaaaaaaaaaaaaaaa 47 -11101010011100 buuuu 47 -11101010011100 yeeeahh 47 -11101010011100 mahaha 47 -11101010011100 annndddd 47 -11101010011100 aigoooo 47 -11101010011100 oooooooooooooooooooo 47 -11101010011100 -enjoy 48 -11101010011100 yaani 48 -11101010011100 waoh 48 -11101010011100 ahnahn 48 -11101010011100 aaaaaaahhh 48 -11101010011100 whoahh 48 -11101010011100 arrh 48 -11101010011100 omigawd 48 -11101010011100 okokokok 48 -11101010011100 dannggg 48 -11101010011100 omgzzz 48 -11101010011100 yeeeeaaa 49 -11101010011100 yeeeaaahhhh 49 -11101010011100 arghhhhhhhhhh 49 -11101010011100 bomat 49 -11101010011100 yeaaaaaaaaaaaa 49 -11101010011100 offt 49 -11101010011100 raah 49 -11101010011100 waaaaaaaaaa 49 -11101010011100 omgggggggggggggg 49 -11101010011100 owowow 49 -11101010011100 rawwwr 49 -11101010011100 wooohh 49 -11101010011100 hollup 49 -11101010011100 congratsssss 49 -11101010011100 woooooooooooooow 50 -11101010011100 yeeeeaaaah 50 -11101010011100 yeeeess 50 -11101010011100 luckyyyyy 50 -11101010011100 nooooooooooooooooooooooooooooo 50 -11101010011100 chaii 50 -11101010011100 wowowowowowow 50 -11101010011100 bwahahha 50 -11101010011100 noooooooooooooooooooooooooooo 51 -11101010011100 ofcoz 51 -11101010011100 yeeeessssss 51 -11101010011100 #gosteelers 51 -11101010011100 &&&&& 51 -11101010011100 yessssssssssssssssssss 51 -11101010011100 waoo 51 -11101010011100 uggggghhh 51 -11101010011100 eeeeeeeeeeeeeee 51 -11101010011100 yahhhhhh 51 -11101010011100 heeeeeeee 52 -11101010011100 aahhhhhhh 52 -11101010011100 omglol 52 -11101010011100 owwwwwwwwwww 52 -11101010011100 aaaaaaaaaaaaaaaa 52 -11101010011100 ooooweeee 52 -11101010011100 yeahhhhhhhhhhhh 52 -11101010011100 daaaaaaaaamn 52 -11101010011100 eurghhh 52 -11101010011100 z0mg 52 -11101010011100 holup 52 -11101010011100 whoehoe 53 -11101010011100 yeaaaaaaaaaaaah 53 -11101010011100 aaaaaahhhhhh 53 -11101010011100 uuuuuh 53 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ewwwwwwwwwwwwww 61 -11101010011100 wai3 61 -11101010011100 yasssssss 62 -11101010011100 hahahhahahahah 62 -11101010011100 wowwwwwwwwww 62 -11101010011100 annndd 62 -11101010011100 woaaaaah 62 -11101010011100 omgaga 62 -11101010011100 wowwwwwwwww 62 -11101010011100 yeeahhh 62 -11101010011100 waahhhh 63 -11101010011100 grrrrrrrrrrrrrrr 63 -11101010011100 ohmg 63 -11101010011100 yay- 63 -11101010011100 haww 63 -11101010011100 omgoshhh 63 -11101010011100 yoooooooooooooo 64 -11101010011100 woaaa 64 -11101010011100 hawww 64 -11101010011100 #15millionbeliebers 64 -11101010011100 unfff 64 -11101010011100 yeeeaahh 64 -11101010011100 omjb 65 -11101010011100 yeoooo 65 -11101010011100 omgeeeee 65 -11101010011100 uuggghhhh 66 -11101010011100 haibo 66 -11101010011100 eeewwwww 66 -11101010011100 dammmmmm 66 -11101010011100 ooooweee 66 -11101010011100 omt 66 -11101010011100 ggrr 67 -11101010011100 yeeeeeeees 67 -11101010011100 yebo 67 -11101010011100 ewwwwwwwwwwwww 67 -11101010011100 ufffff 67 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haaaaaaaaaaaaa 73 -11101010011100 aaaaaahhhhh 73 -11101010011100 yeahhhhhhhhhhh 74 -11101010011100 aaahhhhhhh 74 -11101010011100 noooooooooooooooooooooooooo 74 -11101010011100 oooop 75 -11101010011100 gggrrrr 75 -11101010011100 waaaahhhh 75 -11101010011100 whoaaaaaaa 76 -11101010011100 shiddddd 76 -11101010011100 uuugghhh 77 -11101010011100 weeew 77 -11101010011100 mhen 77 -11101010011100 yaaas 77 -11101010011100 yeeeesssss 77 -11101010011100 ughhhhhhhhhhhhh 77 -11101010011100 eeeeeeeeeeee 78 -11101010011100 ooooooooooooooo 78 -11101010011100 oooooohh 78 -11101010011100 woaahh 79 -11101010011100 yooooooooooooo 79 -11101010011100 coño 79 -11101010011100 #deartwitter 79 -11101010011100 omfgggggg 80 -11101010011100 ooopps 80 -11101010011100 boooooooooooooo 80 -11101010011100 #thesecretcircle 80 -11101010011100 whoooaaaa 80 -11101010011100 ommfg 80 -11101010011100 oyee 80 -11101010011100 ouuuu 80 -11101010011100 ommmmg 81 -11101010011100 nooooooooooooooooooooooo 82 -11101010011100 maaaaaaaan 82 -11101010011100 mannnnnnnnn 82 -11101010011100 oooohhhhhh 82 -11101010011100 noooooooooooooooooooooooo 82 -11101010011100 ugh- 82 -11101010011100 wooooooooooow 82 -11101010011100 arggggg 82 -11101010011100 ssshhhhh 83 -11101010011100 whoooooa 83 -11101010011100 7aram 83 -11101010011100 wowowowowow 83 -11101010011100 oooowwww 85 -11101010011100 nuuuu 86 -11101010011100 eeeeeeeeeee 86 -11101010011100 oweeee 87 -11101010011100 tsek 87 -11101010011100 gaahhh 87 -11101010011100 waaaaaaaaa 87 -11101010011100 arrrrrrr 87 -11101010011100 yallah 89 -11101010011100 damnnnnnnnn 89 -11101010011100 oomg 89 -11101010011100 uughhh 89 -11101010011100 bahhhhh 89 -11101010011100 ayeeeeeeeee 90 -11101010011100 hdu 90 -11101010011100 errrrrrr 90 -11101010011100 owow 90 -11101010011100 woooooooooooow 90 -11101010011100 ooomg 91 -11101010011100 yesssssssssssssssss 91 -11101010011100 urghhhhh 91 -11101010011100 ohhhhhhhhhhhhh 91 -11101010011100 woooowwww 92 -11101010011100 sikeeee 92 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waaaahhh 100 -11101010011100 ughhhhhhhhhhhh 100 -11101010011100 aaaaahhhhhh 100 -11101010011100 aaaaaaaaaaaaaa 100 -11101010011100 ayooooo 101 -11101010011100 c'mon!!! 101 -11101010011100 ekkk 102 -11101010011100 shattap 103 -11101010011100 omn 103 -11101010011100 ooowee 104 -11101010011100 ahhhhhhhhhhhhhhhhhhhh 104 -11101010011100 omgawd 105 -11101010011100 uuggghhh 106 -11101010011100 wahhhhhhh 106 -11101010011100 yeahhhhhhhhhh 107 -11101010011100 yeooo 107 -11101010011100 rahh 108 -11101010011100 nuuu 108 -11101010011100 maaannn 108 -11101010011100 chukkae 108 -11101010011100 wooooww 109 -11101010011100 wowwwwwwww 109 -11101010011100 eeeeeeeeee 110 -11101010011100 yeaaaahhhh 110 -11101010011100 huaaaaa 110 -11101010011100 aaaaaahhhh 112 -11101010011100 #11millionbeliebers 112 -11101010011100 omgs 112 -11101010011100 arhh 113 -11101010011100 rahhh 113 -11101010011100 whoaaaaaa 114 -11101010011100 aaahhhhhh 114 -11101010011100 dayuum 114 -11101010011100 -damn 114 -11101010011100 aaaaaaaaaaaa 115 -11101010011100 yeeeaaahhh 115 -11101010011100 aaaaahh 115 -11101010011100 b-but 115 -11101010011100 omgggggggggg 116 -11101010011100 zomfg 117 -11101010011100 whoooaaa 117 -11101010011100 aghhhhh 118 -11101010011100 ewwwwwwwwwww 118 -11101010011100 yahhhhh 118 -11101010011100 boooooooooooo 118 -11101010011100 maaann 118 -11101010011100 arghhhhhhh 119 -11101010011100 andddddd 119 -11101010011100 omgod 120 -11101010011100 #pickmetila 120 -11101010011100 askies 120 -11101010011100 yoooooooooooo 120 -11101010011100 ommgg 121 -11101010011100 ahhhhhhhhhhhhhhhhhhh 121 -11101010011100 yeeeesss 122 -11101010011100 haaaaaaaaaa 122 -11101010011100 maaaaaaan 123 -11101010011100 omgomgomgomgomg 123 -11101010011100 cumon 123 -11101010011100 noooooooooooooooooooooo 124 -11101010011100 woooooooooow 125 -11101010011100 yayayaya 125 -11101010011100 yeeeessss 125 -11101010011100 kyaaaa 125 -11101010011100 omds 127 -11101010011100 noooooooooooooooooooo 127 -11101010011100 ayyyyyy 128 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424 -11101010011100 omigod 425 -11101010011100 aaaaaaah 433 -11101010011100 ahhhhhhhhhhhh 434 -11101010011100 uugghh 438 -11101010011100 omgz 444 -11101010011100 aaaahhhh 451 -11101010011100 yeeeeee 457 -11101010011100 aigoo 463 -11101010011100 nooooooooooooo 469 -11101010011100 oml 477 -11101010011100 danggg 479 -11101010011100 omgomg 482 -11101010011100 yessssssssss 490 -11101010011100 whoaaaa 494 -11101010011100 duuude 496 -11101010011100 aghhh 500 -11101010011100 ewwwwwww 512 -11101010011100 mannnnnn 514 -11101010011100 aaaahhh 519 -11101010011100 maaaaan 528 -11101010011100 aghh 536 -11101010011100 wow- 542 -11101010011100 woooooow 551 -11101010011100 ps- 551 -11101010011100 ahhhhhhhhhhh 556 -11101010011100 tjo 562 -11101010011100 ooooooooo 569 -11101010011100 andddd 577 -11101010011100 daaaamn 578 -11101010011100 aiya 581 -11101010011100 yooooooo 585 -11101010011100 aaaaaa 590 -11101010011100 damnnnnn 591 -11101010011100 noooooooooooo 604 -11101010011100 wowwwww 610 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oooooooooooooooh 41 -111010100111010 oeeeh 42 -111010100111010 iyes 42 -111010100111010 awwz 42 -111010100111010 okeeh 42 -111010100111010 awwk 42 -111010100111010 umhmm 42 -111010100111010 yupe 43 -111010100111010 awwr 43 -111010100111010 awhhhhhh 43 -111010100111010 hyee 43 -111010100111010 amiiiiin 43 -111010100111010 nanai 44 -111010100111010 sisis 44 -111010100111010 awwwwhh 44 -111010100111010 gheghe 44 -111010100111010 awah 44 -111010100111010 ooooooohhhh 45 -111010100111010 aaaawn 45 -111010100111010 heeya 45 -111010100111010 flwd 45 -111010100111010 yhup 45 -111010100111010 iyaw 45 -111010100111010 owwwwn 46 -111010100111010 #dearjonasbrothers 46 -111010100111010 awwwwwwwwwwwwwwwwww 46 -111010100111010 alooo 46 -111010100111010 aaaaawwww 46 -111010100111010 ohright 46 -111010100111010 awwhhhh 46 -111010100111010 -okay 46 -111010100111010 shnu 46 -111010100111010 ohhhhhhhhhhhhhhh 47 -111010100111010 yalor 48 -111010100111010 oohhhhhh 48 -111010100111010 awwwwwwh 48 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aaawn 77 -111010100111010 oowh 77 -111010100111010 oalah 77 -111010100111010 ahaam 77 -111010100111010 tapalot 78 -111010100111010 ohhhhhhhhhhhhhh 78 -111010100111010 aaaaaaw 79 -111010100111010 dawww 79 -111010100111010 ount 79 -111010100111010 okieee 80 -111010100111010 ohaha 80 -111010100111010 daww 82 -111010100111010 awnn 82 -111010100111010 waaaw 82 -111010100111010 awwl 84 -111010100111010 ooooook 86 -111010100111010 aaaaww 86 -111010100111010 n'aw 86 -111010100111010 awwwee 90 -111010100111010 ahwww 93 -111010100111010 )// 94 -111010100111010 aawn 95 -111010100111010 awwwwwwwwwwwwwww 96 -111010100111010 okeeeee 98 -111010100111010 ooooow 99 -111010100111010 uhum 99 -111010100111010 oooooohhhhh 100 -111010100111010 bendito 100 -111010100111010 awwwwwh 103 -111010100111010 o0o0o 103 -111010100111010 oooown 103 -111010100111010 vixi 103 -111010100111010 percebi 104 -111010100111010 aawh 104 -111010100111010 owwwn 106 -111010100111010 ooooooooooooh 106 -111010100111010 wokay 106 -111010100111010 ouw 107 -111010100111010 oooooohhhh 108 -111010100111010 awr 108 -111010100111010 okeeey 108 -111010100111010 yoyoy 109 -111010100111010 awweee 110 -111010100111010 n'awww 111 -111010100111010 aweeeee 111 -111010100111010 d'awww 112 -111010100111010 aaaawwww 113 -111010100111010 -txt- 116 -111010100111010 awhhhhh 121 -111010100111010 oooooohhh 121 -111010100111010 ohhhhhhhhhhhh 122 -111010100111010 awws 124 -111010100111010 okidoki 126 -111010100111010 oooooooooooh 127 -111010100111010 awwwhhh 127 -111010100111010 ownt 128 -111010100111010 oohhhhh 133 -111010100111010 ohok 134 -111010100111010 awwwwwwwwwwwwww 135 -111010100111010 awwwwn 136 -111010100111010 d'aww 139 -111010100111010 ohhhhhhhhhhh 143 -111010100111010 tabom 146 -111010100111010 aaaawww 146 -111010100111010 ooooohhhhh 148 -111010100111010 oooook 151 -111010100111010 awwhhh 152 -111010100111010 yeapp 155 -111010100111010 iyoo 156 -111010100111010 arey 160 -111010100111010 awwwhh 161 -111010100111010 aaaaaw 163 -111010100111010 awwwwe 164 -111010100111010 jawel 164 -111010100111010 claroo 165 -111010100111010 ouhh 165 -111010100111010 aweh 166 -111010100111010 yeeeh 167 -111010100111010 awwwwwwwwwwwww 174 -111010100111010 ahww 178 -111010100111010 ohw 179 -111010100111010 iyeee 182 -111010100111010 ooooooooooh 183 -111010100111010 ooooohh 185 -111010100111010 okis 185 -111010100111010 oooow 191 -111010100111010 owwn 193 -111010100111010 oown 198 -111010100111010 ohhhhhhhhhh 199 -111010100111010 n'aww 200 -111010100111010 ooown 202 -111010100111010 oooooooooooo 204 -111010100111010 neeee 205 -111010100111010 owhh 206 -111010100111010 dankjee 206 -111010100111010 ouuu 208 -111010100111010 gdgd 210 -111010100111010 aaawwww 211 -111010100111010 la2 215 -111010100111010 oeh 217 -111010100111010 iyaaaa 221 -111010100111010 aawwww 221 -111010100111010 awwwwwwwwwwww 221 -111010100111010 oooohhhhh 224 -111010100111010 okedeh 225 -111010100111010 okeey 227 -111010100111010 awwee 232 -111010100111010 ooooohhhh 244 -111010100111010 ooohhhhh 248 -111010100111010 oooooooooh 253 -111010100111010 awwwn 258 -111010100111010 ooooooooooo 260 -111010100111010 awhhhh 281 -111010100111010 thanks- 285 -111010100111010 ooooohhh 291 -111010100111010 okeeee 293 -111010100111010 aweeee 295 -111010100111010 aaaww 303 -111010100111010 awwn 317 -111010100111010 ohhhhhhhhh 319 -111010100111010 o0o 330 -111010100111010 siip 331 -111010100111010 awwwwwwwwwww 341 -111010100111010 ouu 343 -111010100111010 ooow 344 -111010100111010 aaaaw 352 -111010100111010 awwwwh 358 -111010100111010 oooooooooo 390 -111010100111010 uhuh 395 -111010100111010 okeoke 406 -111010100111010 oohhhh 409 -111010100111010 awwhh 410 -111010100111010 ahw 411 -111010100111010 awwwwwwwwww 430 -111010100111010 imy2 446 -111010100111010 awwwe 454 -111010100111010 ooooooooh 467 -111010100111010 oook 493 -111010100111010 oooohh 499 -111010100111010 oooohhhh 510 -111010100111010 neee 518 -111010100111010 aaawww 534 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oohh 2610 -111010100111010 okee 2916 -111010100111010 awwwwww 3207 -111010100111010 oooooh 3431 -111010100111010 ohhhhh 3899 -111010100111010 ooooo 4731 -111010100111010 awwwww 7587 -111010100111010 oke 8083 -111010100111010 ooooh 8559 -111010100111010 awh 8620 -111010100111010 oooo 9777 -111010100111010 ohhhh 10020 -111010100111010 awe 12335 -111010100111010 oo 15460 -111010100111010 ooo 17204 -111010100111010 oooh 20310 -111010100111010 awwww 21237 -111010100111010 ohhh 24536 -111010100111010 ooh 30993 -111010100111010 aw 47912 -111010100111010 aww 85461 -111010100111010 awww 59276 -111010100111011 uoh 40 -111010100111011 linga 40 -111010100111011 nadaaa 40 -111010100111011 woee 40 -111010100111011 awky 41 -111010100111011 aey 41 -111010100111011 yelah 41 -111010100111011 7ada 42 -111010100111011 ogh 42 -111010100111011 apakan 42 -111010100111011 oah 43 -111010100111011 eerr 43 -111010100111011 ommmm 43 -111010100111011 ghad 43 -111010100111011 kimak 43 -111010100111011 chhh 43 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#godismyhero 61 -111010100111011 aduhh 61 -111010100111011 ewo 61 -111010100111011 wohh 62 -111010100111011 woha 63 -111010100111011 yown 63 -111010100111011 meeny 63 -111010100111011 roight 63 -111010100111011 06/03 64 -111010100111011 allz 64 -111010100111011 oou 64 -111010100111011 oef 65 -111010100111011 eeny 66 -111010100111011 pergh 68 -111010100111011 kaii 68 -111010100111011 oleee 69 -111010100111011 hach 69 -111010100111011 huuuu 69 -111010100111011 shooooo 69 -111010100111011 eeehhh 70 -111010100111011 onga 70 -111010100111011 dahhhh 71 -111010100111011 deeeee 71 -111010100111011 ouais 71 -111010100111011 weeeh 72 -111010100111011 eeeeeh 72 -111010100111011 whai 72 -111010100111011 bref 72 -111010100111011 aaaaaaaaaaaaaaah 73 -111010100111011 wlao 74 -111010100111011 gth 74 -111010100111011 aigo 75 -111010100111011 tyb 75 -111010100111011 oboy 76 -111010100111011 uyyy 78 -111010100111011 ah- 78 -111010100111011 baaaa 80 -111010100111011 auw 81 -111010100111011 haaaah 81 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220 -111010100111011 emmmm 224 -111010100111011 uch 233 -111010100111011 oho 237 -111010100111011 nananana 253 -111010100111011 uhn 256 -111010100111011 huu 265 -111010100111011 nein 269 -111010100111011 och 269 -111010100111011 ohoh 307 -111010100111011 puh 309 -111010100111011 yaaah 316 -111010100111011 shooo 318 -111010100111011 calgon 328 -111010100111011 yalla 332 -111010100111011 eeeh 342 -111010100111011 okeh 376 -111010100111011 noway 401 -111010100111011 arre 409 -111010100111011 muchos 420 -111010100111011 cge 428 -111010100111011 geee 436 -111010100111011 tuhh 438 -111010100111011 oga 474 -111010100111011 chale 481 -111010100111011 mehhh 484 -111010100111011 yeye 484 -111010100111011 sige 496 -111010100111011 eyy 516 -111010100111011 basta 535 -111010100111011 anong 546 -111010100111011 emmm 564 -111010100111011 siao 568 -111010100111011 eeh 575 -111010100111011 fak 576 -111010100111011 haba 630 -111010100111011 erg 648 -111010100111011 whata 668 -111010100111011 chey 673 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bzzzz 53 -11101010011110 @nagasaginomiya 53 -11101010011110 adoi 53 -11101010011110 mmhhh 53 -11101010011110 #asktaylor 55 -11101010011110 1ove 55 -11101010011110 hhhhmmm 55 -11101010011110 aiyooo 55 -11101010011110 awts 55 -11101010011110 psssssst 55 -11101010011110 uuummmm 55 -11101010011110 hrrm 56 -11101010011110 -sincerely 58 -11101010011110 lessee 58 -11101010011110 anddddddd 59 -11101010011110 hoamm 59 -11101010011110 matterfact 59 -11101010011110 lmf 59 -11101010011110 howev 60 -11101010011110 mmmmk 60 -11101010011110 uuumm 60 -11101010011110 hufff 61 -11101010011110 -shrugs 61 -11101010011110 yup2 61 -11101010011110 alaaa 61 -11101010011110 pshht 62 -11101010011110 hhmmmmm 62 -11101010011110 ooopss 62 -11101010011110 hiksss 62 -11101010011110 annddd 63 -11101010011110 urmmm 63 -11101010011110 biogen 63 -11101010011110 wayment 64 -11101010011110 uuuhhhh 65 -11101010011110 hmmh 65 -11101010011110 hmmz 65 -11101010011110 hhhmmmmm 65 -11101010011110 hrmmmm 65 -11101010011110 hikz 66 -11101010011110 buuuttt 66 -11101010011110 hikss 66 -11101010011110 hmms 66 -11101010011110 merrr 67 -11101010011110 btws 67 -11101010011110 y'see 67 -11101010011110 heh-heh 67 -11101010011110 lolno 68 -11101010011110 loldogs 68 -11101010011110 mmmmmh 69 -11101010011110 hmmmmmmmmmmmmm 71 -11101010011110 hhhhmmmm 71 -11101010011110 ummmmmmmmmm 72 -11101010011110 nuhh 72 -11101010011110 haizzz 72 -11101010011110 mmmhh 72 -11101010011110 anywhooo 72 -11101010011110 uhhhm 72 -11101010011110 uhhmmm 72 -11101010011110 orrrrrr 73 -11101010011110 ahmm 73 -11101010011110 huum 73 -11101010011110 wewww 74 -11101010011110 #mentionyourcrush 75 -11101010011110 huhuh 75 -11101010011110 errmmm 76 -11101010011110 btdubs 76 -11101010011110 errrrm 76 -11101010011110 althou 77 -11101010011110 mmmmmmmmmmmmmmm 77 -11101010011110 nway 77 -11101010011110 nyways 78 -11101010011110 hummmmmm 78 -11101010011110 zzzzzzzzzzzzzzz 79 -11101010011110 mmmhhh 79 -11101010011110 abcdefghijklmnopqrstuvwxyz 80 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631 -111010110111110 635 -111010110111110 #50thingsilove 649 -111010110111110 #worldsthinnestbooks 649 -111010110111110 #twothingsthatdontmix 649 -111010110111110 662 -111010110111110 #ilove 670 -111010110111110 #loa 705 -111010110111110 #classicmoviequotes 710 -111010110111110 #thingsilove 720 -111010110111110 joong 724 -111010110111110 ☼ 730 -111010110111110 739 -111010110111110 v: 756 -111010110111110 #hoodmemories 761 -111010110111110  772 -111010110111110 paging 777 -111010110111110 #mj 778 -111010110111110 johann 849 -111010110111110 #thingsilike 856 -111010110111110 nowplaying 877 -111010110111110 #twitraxtuesday 929 -111010110111110 #lyrics 955 -111010110111110 #nowplayin 989 -111010110111110 #imfromteam 998 -111010110111110 #oneletteroffmovies 1004 -111010110111110 #wisdom 1047 -111010110111110 ♫♪ 1048 -111010110111110 #nowlistening 1096 -111010110111110 #imattractedto 1157 -111010110111110 tzu 1171 -111010110111110 #10thingsifindattractive 1238 -111010110111110 escuchando 1294 -111010110111110 a.k.a. 1332 -111010110111110 otm 1441 -111010110111110 ♪♫ 1451 -111010110111110 a.k.a 1528 -111010110111110 ouvindo 1887 -111010110111110 ‎ 1933 -111010110111110 #listen 2077 -111010110111110 #igrewupon 2192 -111010110111110 #rip 2421 -111010110111110 #newmusic 2682 -111010110111110 proverbs 3694 -111010110111110 3887 -111010110111110 #quotes 5150 -111010110111110 5457 -111010110111110 #nowwatching 5945 -111010110111110 starring 7513 -111010110111110 #mm 8029 -111010110111110 #nw 11085 -111010110111110 #musicmonday 15483 -111010110111110 ♪ 21686 -111010110111110 #quote 21927 -111010110111110 aka 26889 -111010110111110 #nowplaying 116082 -111010110111110 #np 64698 -1110101101111110 #qqsm 41 -1110101101111110 0_ 41 -1110101101111110 ****************** 41 -1110101101111110 #canbefoundatwalmart 42 -1110101101111110 *•* 42 -1110101101111110 ↔ 42 -1110101101111110 :::::::::: 42 -1110101101111110 [@ 42 -1110101101111110 43 -1110101101111110 /f 43 -1110101101111110 ▻ 43 -1110101101111110 (|) 44 -1110101101111110 @plz2bemyfriend 44 -1110101101111110 -kids 44 -1110101101111110 ｜ 45 -1110101101111110 37:23 45 -1110101101111110 nagila 45 -1110101101111110 *$* 45 -1110101101111110 #foamparty 45 -1110101101111110 46 -1110101101111110 (_/_) 46 -1110101101111110 (*-*) 47 -1110101101111110 @bigstartv 47 -1110101101111110 48 -1110101101111110 **************** 48 -1110101101111110 #131 49 -1110101101111110 #whoshotwacka 51 -1110101101111110 -------------------- 52 -1110101101111110 ------------------- 52 -1110101101111110 ++++++ 52 -1110101101111110 *************** 53 -1110101101111110 #jr 54 -1110101101111110 8u 56 -1110101101111110 ♍ 58 -1110101101111110 ddddd 59 -1110101101111110 ------------------ 61 -1110101101111110 #radioyoutube 62 -1110101101111110 ()) 62 -1110101101111110 cout 63 -1110101101111110 #tch 63 -1110101101111110 63 -1110101101111110 *+* 64 -1110101101111110 65 -1110101101111110 66 -1110101101111110 ▓ 66 -1110101101111110 66 -1110101101111110 ************** 67 -1110101101111110 cars-trucks 67 -1110101101111110 ▷ 68 -1110101101111110 x40 68 -1110101101111110 @mychaos 69 -1110101101111110 ----------------- 71 -1110101101111110 ~~< 71 -1110101101111110 71 -1110101101111110 x50 73 -1110101101111110 eshgh 73 -1110101101111110 74 -1110101101111110 #jyjinseoul 75 -1110101101111110 }] 76 -1110101101111110 (_|_) 77 -1110101101111110 ▽ 77 -1110101101111110 \\\ 81 -1110101101111110 ::] 82 -1110101101111110 ************* 83 -1110101101111110 #justaskyogirl 86 -1110101101111110 ---------------- 87 -1110101101111110 ::::::::: 87 -1110101101111110 (´▽`)/ 88 -1110101101111110 ‹ 91 -1110101101111110 ]} 93 -1110101101111110 94 -1110101101111110 >… 100 -1110101101111110 -| 100 -1110101101111110 spinoza 101 -1110101101111110 :::::::: 101 -1110101101111110 *} 105 -1110101101111110 -.-- 109 -1110101101111110 *¨* 113 -1110101101111110 \/\/ 114 -1110101101111110 #here 115 -1110101101111110 -** 116 -1110101101111110 ************ 117 -1110101101111110 beasiswa 118 -1110101101111110 119 -1110101101111110 #cme 124 -1110101101111110 -y 127 -1110101101111110 dddd 127 -1110101101111110 **) 129 -1110101101111110 ($) 131 -1110101101111110 131 -1110101101111110 132 -1110101101111110 piku 133 -1110101101111110 #classicgames 137 -1110101101111110 ------------- 138 -1110101101111110 *********** 140 -1110101101111110 #freemaxb 140 -1110101101111110 ]]] 141 -1110101101111110 m/ 144 -1110101101111110 ++++ 145 -1110101101111110 149 -1110101101111110 149 -1110101101111110 /\ 151 -1110101101111110 %) 151 -1110101101111110 ]* 171 -1110101101111110 _o 171 -1110101101111110 -face 171 -1110101101111110 ^) 171 -1110101101111110 */ 172 -1110101101111110 ********** 173 -1110101101111110 -*- 175 -1110101101111110 (@) 179 -1110101101111110 purmerend 187 -1110101101111110 ----------- 199 -1110101101111110 ********* 211 -1110101101111110 ● 212 -1110101101111110 }: 213 -1110101101111110 × 225 -1110101101111110 /* 228 -1110101101111110 ✖ 246 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-1110101101111111 89 -1110101101111111 <--> 89 -1110101101111111 ----------------> 94 -1110101101111111 @brybeats 95 -1110101101111111 »»»» 96 -1110101101111111 ====>> 96 -1110101101111111 >>>>>>>>>>>>>>>>>>>>>>>> 102 -1110101101111111 <<<<<<<<<<<<<<<<<<< 102 -1110101101111111 ─ 109 -1110101101111111 ========> 109 -1110101101111111 ---->>>> 109 -1110101101111111 ------>> 110 -1110101101111111 ---------------> 111 -1110101101111111 ▶▶ 111 -1110101101111111 @jinxbeatz 112 -1110101101111111 #drivethrudiet 112 -1110101101111111 >>>>>" 114 -1110101101111111 >>>>>>>>>>>>>>>>>>>>>>> 114 -1110101101111111 【► 115 -1110101101111111 ~~~~~> 117 -1110101101111111 119 -1110101101111111 ➪ 120 -1110101101111111 --->>>> 122 -1110101101111111 #screenmuncher 122 -1110101101111111 ::::::: 125 -1110101101111111 #140blood 125 -1110101101111111 <<<<<<<<<<<<<<<<< 125 -1110101101111111 #imabzzagent 127 -1110101101111111 -------------- 128 -1110101101111111 129 -1110101101111111 >>>>>>>>>>>>>>>>>>>>>> 133 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-11101011100001 55 -11101011100001 57 -11101011100001 61 -11101011100001 61 -11101011100001 #bethaderej 63 -11101011100001 65 -11101011100001 66 -11101011100001 67 -11101011100001 67 -11101011100001 68 -11101011100001 69 -11101011100001 71 -11101011100001 72 -11101011100001 #lifeq 75 -11101011100001 76 -11101011100001 77 -11101011100001 78 -11101011100001 79 -11101011100001 80 -11101011100001 #twvt 83 -11101011100001 86 -11101011100001 88 -11101011100001 90 -11101011100001 #p2000 90 -11101011100001 91 -11101011100001 93 -11101011100001 95 -11101011100001 103 -11101011100001 104 -11101011100001 133 -11101011100001 134 -11101011100001 145 -11101011100001 145 -11101011100001 160 -11101011100001 169 -11101011100001 173 -11101011100001 183 -11101011100001 188 -11101011100001 212 -11101011100001 #basketballproblems 216 -11101011100001 220 -11101011100001 231 -11101011100001 232 -11101011100001 246 -11101011100001 253 -11101011100001 254 -11101011100001 413 -11101011100001 461 -11101011100001 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#b2p 96 -111010111000100 schonbek 97 -111010111000100 motorcraft 99 -111010111000100 quality-built 103 -111010111000100 coverking 115 -111010111000100 weathershield 115 -111010111000100 merrry 115 -111010111000100 ksport 129 -111010111000100 duragadget 129 -111010111000100 projector-gear 137 -111010111000100 covercraft 139 -111010111000100 raybestos 141 -111010111000100 mery 162 -111010111000100 skinit 204 -111010111000100 238 -111010111000100 4n1 284 -111010111000100 hqrp 299 -111010111000100 phylrich 300 -111010111000100 315 -111010111000100 2n1 546 -111010111000100 powwer 706 -111010111000100 duracell 742 -111010111000100 #gameswithfriends 773 -111010111000100 #orangotag 816 -111010111000100 931 -111010111000100 battpit™ 1068 -111010111000100 sterlingtek's 1245 -111010111000100 hi-capacity 1432 -111010111000100 #runkeeper 2025 -111010111000100 wallmonkeys 3311 -111010111000100 merry 42589 -111010111000100 ▸ 11331 -111010111000101 #dee 41 -111010111000101 41 -111010111000101 fuckety 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-111010111000110 batangyagit 42 -111010111000110 uchari 43 -111010111000110 $2150 43 -111010111000110 $2750 45 -111010111000110 techstream 46 -111010111000110 angelisophia 46 -111010111000110 theodp 47 -111010111000110 (t_t) 48 -111010111000110 50 -111010111000110 $3300 50 -111010111000110 52 -111010111000110 mahreez 52 -111010111000110 asian_dating 53 -111010111000110 56 -111010111000110 $1950 57 -111010111000110 #devnull 57 -111010111000110 $2250 60 -111010111000110 $2900 66 -111010111000110 @fire_flybot 71 -111010111000110 75 -111010111000110 $1550 76 -111010111000110 @null 89 -111010111000110 $1850 96 -111010111000110 #uolheat3 96 -111010111000110 $1750 96 -111010111000110 $1150 96 -111010111000110 $1450 104 -111010111000110 114 -111010111000110 landedon 120 -111010111000110 $2100 131 -111010111000110 $1350 133 -111010111000110 $1650 145 -111010111000110 $1250 158 -111010111000110 riajose 172 -111010111000110 $2200 190 -111010111000110 229 -111010111000110 247 -111010111000110 250 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87 -11101011101111 87 -11101011101111 87 -11101011101111 87 -11101011101111 87 -11101011101111 87 -11101011101111 88 -11101011101111 88 -11101011101111 #defrag08 88 -11101011101111 88 -11101011101111 89 -11101011101111 89 -11101011101111 89 -11101011101111 89 -11101011101111 #wcto08 90 -11101011101111 90 -11101011101111 90 -11101011101111 #socap08 90 -11101011101111 90 -11101011101111 90 -11101011101111 90 -11101011101111 91 -11101011101111 91 -11101011101111 91 -11101011101111 91 -11101011101111 91 -11101011101111 91 -11101011101111 92 -11101011101111 92 -11101011101111 92 -11101011101111 92 -11101011101111 93 -11101011101111 #smclondon08 93 -11101011101111 93 -11101011101111 93 -11101011101111 93 -11101011101111 93 -11101011101111 93 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 -11101011101111 94 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-11101011101111 102 -11101011101111 102 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 103 -11101011101111 104 -11101011101111 104 -11101011101111 #tat09 104 -11101011101111 104 -11101011101111 104 -11101011101111 #w2s 104 -11101011101111 104 -11101011101111 104 -11101011101111 104 -11101011101111 105 -11101011101111 105 -11101011101111 #tcamp09 105 -11101011101111 106 -11101011101111 106 -11101011101111 106 -11101011101111 106 -11101011101111 106 -11101011101111 107 -11101011101111 107 -11101011101111 107 -11101011101111 #firstthingidointhemorning 107 -11101011101111 107 -11101011101111 107 -11101011101111 108 -11101011101111 108 -11101011101111 108 -11101011101111 acim 108 -11101011101111 108 -11101011101111 s01e07 108 -11101011101111 108 -11101011101111 108 -11101011101111 108 -11101011101111 109 -11101011101111 109 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-11101011101111 #whitepeoplegooglesearches 117 -11101011101111 117 -11101011101111 118 -11101011101111 118 -11101011101111 118 -11101011101111 118 -11101011101111 119 -11101011101111 119 -11101011101111 119 -11101011101111 120 -11101011101111 120 -11101011101111 120 -11101011101111 120 -11101011101111 121 -11101011101111 121 -11101011101111 122 -11101011101111 #griots 122 -11101011101111 122 -11101011101111 122 -11101011101111 123 -11101011101111 123 -11101011101111 123 -11101011101111 124 -11101011101111 #bcberlin3 124 -11101011101111 124 -11101011101111 124 -11101011101111 124 -11101011101111 125 -11101011101111 125 -11101011101111 125 -11101011101111 126 -11101011101111 126 -11101011101111 126 -11101011101111 127 -11101011101111 127 -11101011101111 127 -11101011101111 128 -11101011101111 128 -11101011101111 128 -11101011101111 129 -11101011101111 129 -11101011101111 129 -11101011101111 s01e08 129 -11101011101111 129 -11101011101111 130 -11101011101111 130 -11101011101111 130 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-11101011101111 #haikuchallenge 147 -11101011101111 147 -11101011101111 147 -11101011101111 #icny 147 -11101011101111 147 -11101011101111 148 -11101011101111 148 -11101011101111 148 -11101011101111 148 -11101011101111 148 -11101011101111 150 -11101011101111 151 -11101011101111 151 -11101011101111 151 -11101011101111 151 -11101011101111 152 -11101011101111 152 -11101011101111 153 -11101011101111 153 -11101011101111 155 -11101011101111 155 -11101011101111 155 -11101011101111 156 -11101011101111 156 -11101011101111 156 -11101011101111 156 -11101011101111 156 -11101011101111 157 -11101011101111 157 -11101011101111 157 -11101011101111 158 -11101011101111 158 -11101011101111 158 -11101011101111 s01e06 159 -11101011101111 159 -11101011101111 160 -11101011101111 160 -11101011101111 160 -11101011101111 s01e05 161 -11101011101111 161 -11101011101111  161 -11101011101111 161 -11101011101111 161 -11101011101111 162 -11101011101111 162 -11101011101111 163 -11101011101111 163 -11101011101111 163 -11101011101111 163 -11101011101111 163 -11101011101111 165 -11101011101111 166 -11101011101111 #sexualpickuplines 166 -11101011101111 166 -11101011101111 166 -11101011101111 166 -11101011101111 167 -11101011101111 168 -11101011101111 168 -11101011101111 169 -11101011101111 169 -11101011101111 169 -11101011101111 170 -11101011101111 171 -11101011101111 171 -11101011101111 172 -11101011101111 173 -11101011101111 #oow08 173 -11101011101111 174 -11101011101111 174 -11101011101111 175 -11101011101111 175 -11101011101111 176 -11101011101111 176 -11101011101111 178 -11101011101111 178 -11101011101111 179 -11101011101111 #140tc 179 -11101011101111 179 -11101011101111 179 -11101011101111 181 -11101011101111 181 -11101011101111 181 -11101011101111 182 -11101011101111 183 -11101011101111 185 -11101011101111 185 -11101011101111 185 -11101011101111 187 -11101011101111 188 -11101011101111 188 -11101011101111 188 -11101011101111 190 -11101011101111 190 -11101011101111 190 -11101011101111 190 -11101011101111 #web2summit 191 -11101011101111 s01e03 192 -11101011101111 193 -11101011101111 194 -11101011101111 194 -11101011101111 195 -11101011101111 195 -11101011101111 196 -11101011101111 196 -11101011101111 196 -11101011101111 198 -11101011101111 198 -11101011101111 198 -11101011101111 199 -11101011101111 199 -11101011101111 200 -11101011101111 201 -11101011101111 202 -11101011101111 203 -11101011101111 203 -11101011101111 • 203 -11101011101111 204 -11101011101111 205 -11101011101111 208 -11101011101111 209 -11101011101111 209 -11101011101111 210 -11101011101111 211 -11101011101111 212 -11101011101111 212 -11101011101111 213 -11101011101111 214 -11101011101111 216 -11101011101111 217 -11101011101111 219 -11101011101111 219 -11101011101111 #tas08 220 -11101011101111 220 -11101011101111 221 -11101011101111 s01e02 222 -11101011101111 222 -11101011101111 224 -11101011101111 225 -11101011101111 228 -11101011101111 228 -11101011101111 228 -11101011101111 229 -11101011101111 229 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-11101011101111 276 -11101011101111 277 -11101011101111 278 -11101011101111 281 -11101011101111 281 -11101011101111 282 -11101011101111 283 -11101011101111 284 -11101011101111 286 -11101011101111 287 -11101011101111 289 -11101011101111 290 -11101011101111 290 -11101011101111 osho 292 -11101011101111 292 -11101011101111 #bwe09 295 -11101011101111 300 -11101011101111 300 -11101011101111 s01e01 300 -11101011101111 301 -11101011101111 304 -11101011101111 309 -11101011101111 310 -11101011101111 312 -11101011101111 316 -11101011101111 320 -11101011101111 327 -11101011101111 328 -11101011101111 331 -11101011101111 333 -11101011101111 333 -11101011101111 336 -11101011101111 340 -11101011101111 340 -11101011101111 ★★★★ 344 -11101011101111 345 -11101011101111 352 -11101011101111 352 -11101011101111 354 -11101011101111 354 -11101011101111 360 -11101011101111 361 -11101011101111 362 -11101011101111 362 -11101011101111 370 -11101011101111 370 -11101011101111 373 -11101011101111 375 -11101011101111 #w2e 378 -11101011101111 379 -11101011101111 382 -11101011101111 386 -11101011101111 387 -11101011101111 389 -11101011101111 392 -11101011101111 393 -11101011101111 393 -11101011101111 394 -11101011101111 397 -11101011101111 400 -11101011101111 402 -11101011101111 405 -11101011101111 405 -11101011101111 409 -11101011101111 #ces09 411 -11101011101111 -h 411 -11101011101111 414 -11101011101111 418 -11101011101111 422 -11101011101111 423 -11101011101111 423 -11101011101111 425 -11101011101111 426 -11101011101111 #wds08 431 -11101011101111 431 -11101011101111 435 -11101011101111 438 -11101011101111 #unansweredhiphopquestions 446 -11101011101111 452 -11101011101111 461 -11101011101111 461 -11101011101111 466 -11101011101111 469 -11101011101111 470 -11101011101111 under """ + fpath[1] + """ -
  • --> click """ + fpath[2] + """
    • -
  • --> then find """ + fpath[3] + """
    • -
  • --> click VIEW
    • -
  • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
    • - -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      - - - """ - subject = 'Your Reddit Comment collection has been terminated...' - elif case == 2 or case == 'comment-success': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is ready for you!

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. It will be compressed for you in an .zip file named """+ fpath[3] +"""-comments.zip
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] +"""
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your Reddit Comment collection is completed!' - elif case == 3 or case == 'analytics-success': - list_html = '' - for key in links.keys(): - list_html += '
    • ' + key + '
      • ' - - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your """ + fpath[2] + """ results are ready for you! (job ID: """ + fpath[3] + """)

      -
        -
      • You can view the visualization and download the results at HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """ tab
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click view
          • -
        -
        -
      • You can also click the link below to download part of the results: -
          """ + list_html + """
        -
        • -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your ' + fpath[2] + ' computation is completed!' - - msg = MIMEMultipart('alternative') - msg['Subject'] = subject - msg['From'] = fromaddr - msg['To'] = toaddr - msg.attach(MIMEText(html, 'html')) - - server = smtplib.SMTP_SSL(host, port) - server.login(fromaddr, password) - server.sendmail(fromaddr, toaddr, msg.as_string()) - server.quit() - else: - print("Invalid Email host setting! Skip notification.") diff --git a/name_entity_recognition/plot.py b/name_entity_recognition/plot.py deleted file mode 100644 index 431d2ff..0000000 --- a/name_entity_recognition/plot.py +++ /dev/null @@ -1,157 +0,0 @@ -from plotly.graph_objs import * -from plotly.offline import plot -import networkx as nx - - -def plot_pie_chart(labels, values, title): - """ - plot pie chart - :param labels: list of label, shape must match parameter values - :param values: list of values, shape must match parameter labels - :param title: title to show - :return: html code in a div - """ - trace = Pie(labels=labels, values=values, textinfo='label+percent') - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_network(graph, layout, relationships, title): - """ - plot network graph - :param graph: networkx graph - :param layout: network layout - :param relationships: reply, retweet, mention or anything else - :param title: title to show - :return: html code in a div - """ - - if layout == 'spring': - pos = nx.spring_layout(graph) - elif layout == 'circular': - pos = nx.circular_layout(graph) - elif layout == 'fruchterman': - pos = nx.fruchterman_reingold_layout(graph) - elif layout == 'random': - pos = nx.random_layout(graph) - elif layout == 'shell': - pos = nx.shell_layout(graph) - elif layout == 'spectral': - pos = nx.spectral_layout(graph) - edge_attri = nx.get_edge_attributes(graph, 'text') - edge_trace = Scatter(x=[], y=[], text=[], - line=Line(width=1, color='#b5b5b5'), - hoverinfo='text', - mode='lines', - hoveron='points') - for edge in graph.edges(): - x0, y0 = pos[edge[0]] - x1, y1 = pos[edge[1]] - edge_trace['x'] += [x0, x1, None] - edge_trace['y'] += [y0, y1, None] - edge_trace['text'].append(edge_attri[edge]) - - node_trace = Scatter(x=[], y=[], text=[], mode='markers', - hoverinfo='text', hoveron='points+fills', - marker=Marker(showscale=True, - colorscale='Portland', - reversescale=False, - color=[], - size=10, - colorbar=dict(thickness=15, - title='node in-degree plus out-degree', - xanchor='left', - titleside='right'), - line=dict(width=2))) - for node in graph.nodes(): - x, y = pos[node] - node_trace['x'].append(x) - node_trace['y'].append(y) - - # set label - for node in graph.nodes(): - node_trace['marker']['color'].append(graph.in_degree()[node] + graph.out_degree()[node]) - if relationships == 'reply_to': - node_trace['text'].append("@" + node + " is replied by " - + str(graph.in_degree()[node]) - + " user(s), and replies to " - + str(graph.out_degree()[node]) + " user(s)") - - elif relationships == 'retweet_from': - node_trace['text'].append("@" + node + " is retweeted by " - + str(graph.in_degree()[node]) - + " user(s) and retweets from " - + str(graph.out_degree()[node]) - + " user(s)") - - elif relationships == 'mentions': - node_trace['text'].append("@" + node + " is mentioned by " - + str(graph.in_degree()[node]) - + " user(s) and mentions " - + str(graph.out_degree()[node]) - + " user(s)") - - fig = Figure(data=Data([edge_trace, node_trace]), - layout=Layout( - title=title, - titlefont=dict(size=16), showlegend=False, - hovermode='closest', margin=dict(b=20, l=5, r=5, t=40), - xaxis=XAxis(showgrid=False, zeroline=False, - showticklabels=False), - yaxis=YAxis(showgrid=False, zeroline=False, - showticklabels=False) - )) - - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_bar_chart(index, counts, title): - """ - plot bar chart - :param index: x - axis, usually the index - :param counts: y - axis, usually counts - :param title: - :return: - """ - - trace = Bar(x=index, y=counts, - marker=dict(color='rgba(200,75,73,1.0)', - line=dict(color='rgba(111,11,9,1.0)',width=2))) - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - yaxis=dict( - showgrid=True, - showline=True, - showticklabels=True, - linecolor='rgba(102, 102, 102, 0.8)', - linewidth=2 - ), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, image_filename='plot_img') - - return div diff --git a/name_entity_recognition/postToAWSBatch.py b/name_entity_recognition/postToAWSBatch.py deleted file mode 100644 index c1e54c3..0000000 --- a/name_entity_recognition/postToAWSBatch.py +++ /dev/null @@ -1,25 +0,0 @@ -import boto3 -import os - -client = boto3.client('batch', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(jobDefinition, jobName, jobQueue, command): - - command.extend(['--HOST_IP', os.environ['HOST_IP'], - '--AWS_ACCESSKEY', os.environ['AWS_ACCESSKEY'], - '--AWS_ACCESSKEYSECRET', os.environ['AWS_ACCESSKEYSECRET'], - '--BUCKET_NAME', os.environ['BUCKET_NAME']]) - - response = client.submit_job( - jobDefinition=jobDefinition, - jobName=jobName, - jobQueue=jobQueue, - containerOverrides={ - 'vcpus': 2, - 'memory': 2048, - 'command': command - } - ) - - return response \ No newline at end of file diff --git a/name_entity_recognition/rabbitmq_handler.py b/name_entity_recognition/rabbitmq_handler.py deleted file mode 100644 index 6d2d2c6..0000000 --- a/name_entity_recognition/rabbitmq_handler.py +++ /dev/null @@ -1,67 +0,0 @@ -import json -import os -import traceback -import pika -import postToAWSBatch - - -def rabbitmq_handler(ch, method, properties, body): - try: - msg = {} - - # determine if it goes to aws, lambda, or batch - params = json.loads(body) - - if params['platform'] == 'aws-lambda': - raise ValueError("Not applicable to this algorithm.") - - elif params['platform'] == 'aws-batch': - postToAWSBatch.invoke(params['jobDefinition'], - params['jobName'], - params['jobQueue'], - params['command']) - - elif params['platform'] == 'lambda': - raise ValueError("Not applicable to this algorithm.") - - elif params['platform'] == 'batch': - os.system(' '.join(params['command'])) - - else: - raise ValueError( - 'Rabbitmq Message Not Recognizable. ' - 'It has to specify what platform to run: aws-lambda, aws-batch, lambda or batch.') - - except BaseException as e: - - msg = {'ERROR': - {'message': str(e), - 'traceback': traceback.format_exc() - } - } - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - return None - - -if __name__ == '__main__': - connection = pika.BlockingConnection(pika.ConnectionParameters( - port=5672, - host="rabbitmq", - heartbeat=600, - blocked_connection_timeout=600 - )) - channel = connection.channel() - - # pass the queue name in environment variable - queue = os.environ['QUEUE_NAME'] - - channel.queue_declare(queue=queue) - channel.basic_qos(prefetch_count=1) - channel.basic_consume(queue=queue, on_message_callback=rabbitmq_handler, auto_ack=True) - channel.start_consuming() diff --git a/name_entity_recognition/requirement.txt b/name_entity_recognition/requirement.txt deleted file mode 100644 index 180c265..0000000 --- a/name_entity_recognition/requirement.txt +++ /dev/null @@ -1,16 +0,0 @@ -boto3>=1.6.11 -numpy==1.16.1 -pandas>=0.24.1 -networkx==1.11 -plotly==2.7.0 -future==0.17.1 -gensim==3.7.1 -scikit-learn==0.19.1 -regex==2019.2.21 -matplotlib==2.1.2 -seaborn==0.9.0 -six==1.12.0 -sklearn-crfsuite==0.3.6 -joblib==0.13.2 -pika>=1.1.0 -pyclowder==2.7.0 diff --git a/name_entity_recognition/run_ner.py b/name_entity_recognition/run_ner.py deleted file mode 100644 index 277f5bc..0000000 --- a/name_entity_recognition/run_ner.py +++ /dev/null @@ -1,93 +0,0 @@ -import os -import pickle -import sys - -import six -from features import sent2features, DictionaryFeatures, ClusterFeatures, WordVectors -from utils import load_sequences, process_glovevectors - -PROJECT_DIR = os.path.abspath(os.path.dirname(os.path.realpath(__file__))) -DATA_DIR = os.path.join(PROJECT_DIR, "data") - -TRAIN_FILE = os.path.join(DATA_DIR, "wnut16_train") -DEV_FILE = os.path.join(DATA_DIR, "wnut16_dev") -HEGE_TRAIN_FILE = os.path.join(DATA_DIR, "hege.test.tsv") - -TWITTER_NER_MODEL_FILE = "twitter_ner%s_model.pkl" - -MODEL_DIR = os.path.join(PROJECT_DIR, "models") -DICTIONARY_DIR = os.path.join(DATA_DIR, "cleaned", "custom_lexicons") -WORDVEC_FILE_RAW = os.path.join(DATA_DIR, "glove.twitter.27B.200d.txt") -WORDVEC_FILE_PROCESSED = os.path.join(DATA_DIR, "glove.twitter.27B.200d.txt.processed.txt") -GIMPLE_TWITTER_BROWN_CLUSTERS_DIR = os.path.join(DATA_DIR, "50mpaths2") -TEST_ENRICHED_DATA_BROWN_CLUSTER_DIR = os.path.join(DATA_DIR, "brown_clusters%s") -TEST_ENRICHED_DATA_CLARK_CLUSTER_DIR = os.path.join(DATA_DIR, "clark_clusters%s") - - -class TwitterNER: - def __init__(self, training_data_name="_wnut_and_hege", - train_files=[(TRAIN_FILE, "utf-8"), (HEGE_TRAIN_FILE, "utf-8")]): - model_dir = os.path.join(MODEL_DIR, "python%s" % sys.version_info.major) - with open(os.path.join(model_dir, TWITTER_NER_MODEL_FILE % training_data_name), "rb") as pickle_file: - self.model = pickle.load(pickle_file) - - self.dict_features = DictionaryFeatures(DICTIONARY_DIR) - - all_sequences = load_sequences(DEV_FILE) - for (train_file, encoding) in train_files: - all_sequences.extend(load_sequences(train_file, sep="\t", encoding=encoding)) - all_tokens = [[t[0] for t in seq] for seq in all_sequences] - - if not os.path.exists(WORDVEC_FILE_PROCESSED): - process_glovevectors(WORDVEC_FILE_RAW) - self.word2vec_model = WordVectors(all_tokens, WORDVEC_FILE_PROCESSED) - - gimple_brown_cf = ClusterFeatures(GIMPLE_TWITTER_BROWN_CLUSTERS_DIR, cluster_type="brown") - gimple_brown_cf.set_cluster_file_path(GIMPLE_TWITTER_BROWN_CLUSTERS_DIR) - self.gimple_brown_clusters = gimple_brown_cf.read_clusters() - - test_enriched_data_brown_cluster_dir = TEST_ENRICHED_DATA_BROWN_CLUSTER_DIR % training_data_name - test_enriched_data_brown_cf = ClusterFeatures(test_enriched_data_brown_cluster_dir, - cluster_type="brown", n_clusters=100) - test_enriched_data_brown_cf.set_cluster_file_path() - self.test_enriched_data_brown_clusters = test_enriched_data_brown_cf.read_clusters() - - test_enriched_data_clark_cluster_dir = TEST_ENRICHED_DATA_CLARK_CLUSTER_DIR % training_data_name - test_enriched_data_clark_cf = ClusterFeatures(test_enriched_data_clark_cluster_dir, - cluster_type="clark", n_clusters=32) - test_enriched_data_clark_cf.set_cluster_file_path() - self.test_enriched_data_clark_clusters = test_enriched_data_clark_cf.read_clusters() - - def get_features(self, tokens): - return sent2features(tokens, WORD_IDX=None, vocab=None, - dict_features=self.dict_features, vocab_presence_only=False, - window=4, interactions=True, dict_interactions=True, - lowercase=False, dropout=0, - word2vec_model=self.word2vec_model.model, - cluster_vocabs=[ - self.gimple_brown_clusters, - self.test_enriched_data_brown_clusters, - self.test_enriched_data_clark_clusters - ]) - - def get_entities(self, tokens): - predictions = self.model.predict([self.get_features(tokens)]) - - entities = [] - previous_state = None - entity_start = None - for i in six.moves.range(len(tokens)): - token = tokens[i] - label = predictions[0][i] - state = label[0] - if state in ("B", "U") or \ - (state in ("I", "E") and previous_state not in ("B", "I")): - entity_start = i - if state in ("E", "U") or \ - (state in ("B", "I") and (i == len(tokens) - 1 or predictions[0][i + 1][0] not in ("I", "E"))): - entity_type = label[2:] - if entity_type is not None: - entities.append((entity_start, i + 1, entity_type)) - entity_start = None - previous_state = state - return entities diff --git a/name_entity_recognition/twokenize.py b/name_entity_recognition/twokenize.py deleted file mode 100755 index 12108a0..0000000 --- a/name_entity_recognition/twokenize.py +++ /dev/null @@ -1,313 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Twokenize -- a tokenizer designed for Twitter text in English and some other European languages. -This tokenizer code has gone through a long history: - -(1) Brendan O'Connor wrote original version in Python, http://github.com/brendano/tweetmotif - TweetMotif: Exploratory Search and Topic Summarization for Twitter. - Brendan O'Connor, Michel Krieger, and David Ahn. - ICWSM-2010 (demo track), http://brenocon.com/oconnor_krieger_ahn.icwsm2010.tweetmotif.pdf -(2a) Kevin Gimpel and Daniel Mills modified it for POS tagging for the CMU ARK Twitter POS Tagger -(2b) Jason Baldridge and David Snyder ported it to Scala -(3) Brendan bugfixed the Scala port and merged with POS-specific changes - for the CMU ARK Twitter POS Tagger -(4) Tobi Owoputi ported it back to Java and added many improvements (2012-06) - -Current home is http://github.com/brendano/ark-tweet-nlp and http://www.ark.cs.cmu.edu/TweetNLP - -There have been at least 2 other Java ports, but they are not in the lineage for the code here. - -Ported to Python by Myle Ott . -""" -from __future__ import unicode_literals - -import re -import sys - -try: - from html.parser import HTMLParser -except ImportError: - from HTMLParser import HTMLParser - -try: - import html -except ImportError: - pass - -def regex_or(*items): - return '(?:' + '|'.join(items) + ')' - -Contractions = re.compile(u"(?i)(\w+)(n['’′]t|['’′]ve|['’′]ll|['’′]d|['’′]re|['’′]s|['’′]m)$", re.UNICODE) -Whitespace = re.compile(u"[\s\u0020\u00a0\u1680\u180e\u202f\u205f\u3000\u2000-\u200a]+", re.UNICODE) - -punctChars = r"['\"“”‘’.?!…,:;]" -#punctSeq = punctChars+"+" #'anthem'. => ' anthem '. -punctSeq = r"['\"“”‘’]+|[.?!,…]+|[:;]+" #'anthem'. => ' anthem ' . -entity = r"&(?:amp|lt|gt|quot);" -# URLs - - -# BTO 2012-06: everyone thinks the daringfireball regex should be better, but they're wrong. -# If you actually empirically test it the results are bad. -# Please see https://github.com/brendano/ark-tweet-nlp/pull/9 - -urlStart1 = r"(?:https?://|\bwww\.)" -commonTLDs = r"(?:com|org|edu|gov|net|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|pro|tel|travel|xxx)" -ccTLDs = r"(?:ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|" + \ -r"bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|" + \ -r"er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|" + \ -r"hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|" + \ -r"lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|" + \ -r"nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|sk|" + \ -r"sl|sm|sn|so|sr|ss|st|su|sv|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|" + \ -r"va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|za|zm|zw)" #TODO: remove obscure country domains? -urlStart2 = r"\b(?:[A-Za-z\d-])+(?:\.[A-Za-z0-9]+){0,3}\." + regex_or(commonTLDs, ccTLDs) + r"(?:\."+ccTLDs+r")?(?=\W|$)" -urlBody = r"(?:[^\.\s<>][^\s<>]*?)?" -urlExtraCrapBeforeEnd = regex_or(punctChars, entity) + "+?" -urlEnd = r"(?:\.\.+|[<>]|\s|$)" -url = regex_or(urlStart1, urlStart2) + urlBody + "(?=(?:"+urlExtraCrapBeforeEnd+")?"+urlEnd+")" - - -# Numeric -timeLike = r"\d+(?::\d+){1,2}" -#numNum = r"\d+\.\d+" -numberWithCommas = r"(?:(?|>)[\._-]+(?:<|<|>|>)" -s5 = "(?:[.][_]+[.])" -# myleott: in Python the (?i) flag affects the whole expression -#basicface = "(?:(?i)" +bfLeft+bfCenter+bfRight+ ")|" +s3+ "|" +s4+ "|" + s5 -basicface = "(?:" +bfLeft+bfCenter+bfRight+ ")|" +s3+ "|" +s4+ "|" + s5 - -eeLeft = r"[＼\\ƪԄ\(（<>;ヽ\-=~\*]+" -eeRight= u"[\\-=\\);'\u0022<>ʃ）/／ノﾉ丿╯σっµ~\\*]+" -eeSymbol = r"[^A-Za-z0-9\s\(\)\*:=-]" -eastEmote = eeLeft + "(?:"+basicface+"|" +eeSymbol+")+" + eeRight - -oOEmote = r"(?:[oO]" + bfCenter + r"[oO])" - - -emoticon = regex_or( - # Standard version :) :( :] :D :P - "(?:>|>)?" + regex_or(normalEyes, wink) + regex_or(noseArea,"[Oo]") + regex_or(tongue+r"(?=\W|$|RT|rt|Rt)", otherMouths+r"(?=\W|$|RT|rt|Rt)", sadMouths, happyMouths), - - # reversed version (: D: use positive lookbehind to remove "(word):" - # because eyes on the right side is more ambiguous with the standard usage of : ; - regex_or("(?<=(?: ))", "(?<=(?:^))") + regex_or(sadMouths,happyMouths,otherMouths) + noseArea + regex_or(normalEyes, wink) + "(?:<|<)?", - - #inspired by http://en.wikipedia.org/wiki/User:Scapler/emoticons#East_Asian_style - eastEmote.replace("2", "1", 1), basicface, - # iOS 'emoji' characters (some smileys, some symbols) [\ue001-\uebbb] - # TODO should try a big precompiled lexicon from Wikipedia, Dan Ramage told me (BTO) he does this - - # myleott: o.O and O.o are two of the biggest sources of differences - # between this and the Java version. One little hack won't hurt... - oOEmote -) - -Hearts = "(?:<+/?3+)+" #the other hearts are in decorations - -Arrows = regex_or(r"(?:<*[-―—=]*>+|<+[-―—=]*>*)", u"[\u2190-\u21ff]+") - -# BTO 2011-06: restored Hashtag, AtMention protection (dropped in original scala port) because it fixes -# "hello (#hashtag)" ==> "hello (#hashtag )" WRONG -# "hello (#hashtag)" ==> "hello ( #hashtag )" RIGHT -# "hello (@person)" ==> "hello (@person )" WRONG -# "hello (@person)" ==> "hello ( @person )" RIGHT -# ... Some sort of weird interaction with edgepunct I guess, because edgepunct -# has poor content-symbol detection. - -# This also gets #1 #40 which probably aren't hashtags .. but good as tokens. -# If you want good hashtag identification, use a different regex. -Hashtag = "#[a-zA-Z0-9_]+" #optional: lookbehind for \b -#optional: lookbehind for \b, max length 15 -AtMention = "[@＠][a-zA-Z0-9_]+" - -# I was worried this would conflict with at-mentions -# but seems ok in sample of 5800: 7 changes all email fixes -# http://www.regular-expressions.info/email.html -Bound = r"(?:\W|^|$)" -Email = regex_or("(?<=(?:\W))", "(?<=(?:^))") + r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,4}(?=" +Bound+")" - -# We will be tokenizing using these regexps as delimiters -# Additionally, these things are "protected", meaning they shouldn't be further split themselves. -Protected = re.compile( - regex_or( - Hearts, - url, - Email, - timeLike, - #numNum, - numberWithCommas, - numComb, - emoticon, - Arrows, - entity, - punctSeq, - arbitraryAbbrev, - separators, - decorations, - embeddedApostrophe, - Hashtag, - AtMention), re.UNICODE) - -# Edge punctuation -# Want: 'foo' => ' foo ' -# While also: don't => don't -# the first is considered "edge punctuation". -# the second is word-internal punctuation -- don't want to mess with it. -# BTO (2011-06): the edgepunct system seems to be the #1 source of problems these days. -# I remember it causing lots of trouble in the past as well. Would be good to revisit or eliminate. - -# Note the 'smart quotes' (http://en.wikipedia.org/wiki/Smart_quotes) -#edgePunctChars = r"'\"“”‘’«»{}\(\)\[\]\*&" #add \\p{So}? (symbols) -edgePunctChars = u"'\"“”‘’«»{}\\(\\)\\[\\]\\*&" #add \\p{So}? (symbols) -edgePunct = "[" + edgePunctChars + "]" -notEdgePunct = "[a-zA-Z0-9]" # content characters -offEdge = r"(^|$|:|;|\s|\.|,)" # colon here gets "(hello):" ==> "( hello ):" -EdgePunctLeft = re.compile(offEdge + "("+edgePunct+"+)("+notEdgePunct+")", re.UNICODE) -EdgePunctRight = re.compile("("+notEdgePunct+")("+edgePunct+"+)" + offEdge, re.UNICODE) - -def splitEdgePunct(input): - input = EdgePunctLeft.sub(r"\1\2 \3", input) - input = EdgePunctRight.sub(r"\1 \2\3", input) - return input - -# The main work of tokenizing a tweet. -def simpleTokenize(text): - - # Do the no-brainers first - splitPunctText = splitEdgePunct(text) - - textLength = len(splitPunctText) - - # BTO: the logic here got quite convoluted via the Scala porting detour - # It would be good to switch back to a nice simple procedural style like in the Python version - # ... Scala is such a pain. Never again. - - # Find the matches for subsequences that should be protected, - # e.g. URLs, 1.0, U.N.K.L.E., 12:53 - bads = [] - badSpans = [] - for match in Protected.finditer(splitPunctText): - # The spans of the "bads" should not be split. - if (match.start() != match.end()): #unnecessary? - bads.append( [splitPunctText[match.start():match.end()]] ) - badSpans.append( (match.start(), match.end()) ) - - # Create a list of indices to create the "goods", which can be - # split. We are taking "bad" spans like - # List((2,5), (8,10)) - # to create - # List(0, 2, 5, 8, 10, 12) - # where, e.g., "12" here would be the textLength - # has an even length and no indices are the same - indices = [0] - for (first, second) in badSpans: - indices.append(first) - indices.append(second) - indices.append(textLength) - - # Group the indices and map them to their respective portion of the string - splitGoods = [] - for i in range(0, len(indices), 2): - goodstr = splitPunctText[indices[i]:indices[i+1]] - splitstr = goodstr.strip().split(" ") - splitGoods.append(splitstr) - - # Reinterpolate the 'good' and 'bad' Lists, ensuring that - # additonal tokens from last good item get included - zippedStr = [] - for i in range(len(bads)): - zippedStr = addAllnonempty(zippedStr, splitGoods[i]) - zippedStr = addAllnonempty(zippedStr, bads[i]) - zippedStr = addAllnonempty(zippedStr, splitGoods[len(bads)]) - - # BTO: our POS tagger wants "ur" and "you're" to both be one token. - # Uncomment to get "you 're" - #splitStr = [] - #for tok in zippedStr: - # splitStr.extend(splitToken(tok)) - #zippedStr = splitStr - - return zippedStr - -def addAllnonempty(master, smaller): - for s in smaller: - strim = s.strip() - if (len(strim) > 0): - master.append(strim) - return master - -# "foo bar " => "foo bar" -def squeezeWhitespace(input): - return Whitespace.sub(" ", input).strip() - -# Final pass tokenization based on special patterns -def splitToken(token): - m = Contractions.search(token) - if m: - return [m.group(1), m.group(2)] - return [token] - -# Assume 'text' has no HTML escaping. -def tokenize(text): - return simpleTokenize(squeezeWhitespace(text)) - - -# Twitter text comes HTML-escaped, so unescape it. -# We also first unescape &'s, in case the text has been buggily double-escaped. -def normalizeTextForTagger(text): - text = text.replace("&", "&") - #http://stackoverflow.com/questions/2360598 - #/how-do-i-unescape-html-entities-in-a-string-in-python-3-1 - if sys.version_info[0] > 2: - if sys.version_info[0] == 3 and sys.version_info[1] <= 3: - text = HTMLParser().unescape(text) - else: - text = html.unescape(text) - else: - text = HTMLParser().unescape(text) - return text - -# This is intended for raw tweet text -- we do some HTML entity unescaping before running the tagger. -# -# This function normalizes the input text BEFORE calling the tokenizer. -# So the tokens you get back may not exactly correspond to -# substrings of the original text. -def tokenizeRawTweetText(text): - tokens = tokenize(normalizeTextForTagger(text)) - return tokens diff --git a/name_entity_recognition/utils.py b/name_entity_recognition/utils.py deleted file mode 100644 index 9bba14b..0000000 --- a/name_entity_recognition/utils.py +++ /dev/null @@ -1,232 +0,0 @@ -from __future__ import (absolute_import, division, - print_function, unicode_literals) -from builtins import * -from io import open - -import matplotlib - -matplotlib.use("Agg") -import matplotlib.pyplot as plt -import seaborn as sns -sns.set_context("poster") -sns.set_style("ticks") - -import numpy as np -import pandas as pd - -from collections import namedtuple -import regex as re -import subprocess - -Tag = namedtuple("Tag", ["token", "tag"]) - -OTHER_ENTITIES_KEYS=("isHashtag", "isMention", "isURL", "isMoney", "isNumber", "repeatedPunctuation") -ENTITY_MAPPINGS={k: "__%s__" % k for k in OTHER_ENTITIES_KEYS} - - -def load_sequences(filename, sep="\t", notypes=False, test_data=False, encoding='utf-8'): - sequences = [] - with open(filename, encoding="utf-8", errors="ignore") as fp: - seq = [] - for line in fp: - line = line.strip() - if line: - line = line.split(sep) - if test_data: - assert len(line) == 1 - line.append("?") - if notypes: - line[1] = line[1][0] - seq.append(Tag(*line)) - else: - sequences.append(seq) - seq = [] - if seq: - sequences.append(seq) - return sequences - - -def load_vocab(filename): - vocab = set() - with open(filename, encoding="utf-8", errors="ignore") as fp: - for line in fp: - line = line.strip() - vocab.add(line) - return vocab - - -def get_cat_names(sequences): - cat_names = set() - for seq in sequences: - for t in seq: - if t[1] != "O": - cat_names.add(t[1][2:]) - return cat_names - - - -def print_transitions(trans_features): - for (label_from, label_to), weight in trans_features: - print("%-6s -> %-7s %0.6f" % (label_from, label_to, weight)) - -def print_state_features(state_features): - for (attr, label), weight in state_features: - print("%0.6f %-8s %s" % (weight, label, attr)) - - -def plot_cm(y_test, y_pred, labels=[], axis=1): - labels_s = dict((k,i) for i,k in enumerate(labels)) - cm = np.zeros((len(labels), len(labels))) - for i,j in zip(sum(y_test, []), sum(y_pred, [])): - i = labels_s[i] - j = labels_s[j] - cm[i,j] += 1 - with plt.rc_context(rc={'xtick.labelsize': 12, 'ytick.labelsize': 12, - 'figure.figsize': (16,14)}): - sns.heatmap(cm * 100/ cm.sum(axis=axis, keepdims=True), - #cmap=sns.cubehelix_palette(n_colors=100, rot=-.4, as_cmap=True), - cmap="Greys", - xticklabels=labels, - yticklabels=labels) - plt.ylabel("True labels") - plt.xlabel("Predicted labels") - title = "Precision Plot" - if axis== 0: - title = "Recall Plot" - plt.title(title) - return cm - - -def print_sequences(sequences, predictions, filename, test_data=False, notypes=False): - with open(filename, "w", encoding="utf-8", errors="ignore") as fp: - for seq, pred in zip(sequences, predictions): - for t, p in zip(seq, pred): - token, tag = t - if tag[0] == "U": - tag = "B%s" % tag[1:] - if tag[0] == "E": - tag = "I%s" % tag[1:] - if p[0] == "U": - p = "B%s" % p[1:] - if p[0] == "E": - p = "I%s" % p[1:] - if notypes: - tag = tag[0] - p = p[0] - if test_data: - line = "\t".join((token, p)) - else: - line = "\t".join((token, tag, p)) - print(line, file=fp) - print("", file=fp) - - - -RESULTS_REGEX=re.compile(r'\s+([a-z\-]*):.*?([0-9\.]+)%;.*?([0-9\.]+)%;.*?([0-9\.]+)\s+([0-9]+)') -def parse_results(results): - results = results.splitlines() - result_obj = dict() - result_obj.update((k,v) for k,v in zip(("processed_tokens", "total", "found", "correct"), - re.findall(r'[0-9]+', results[0].strip()))) - overall = re.match(r'.*?([0-9\.]+).*?([0-9\.]+)%;.*?([0-9\.]+)%;.*?\s+([0-9\.]+)', results[1]).groups() - result_obj["overall_accuracy"] = float(overall[0]) - prfs_vals = [("overall",) + tuple(map(float, overall[1:] + (0.,)))] - for line in results[2:]: - if not line.strip(): - continue - items = RESULTS_REGEX.match(line).groups() - prfs_vals.append(items[:1] + tuple(map(float, items[1:]))) - result_obj["prfs"] = pd.DataFrame(prfs_vals, columns=["category", "precision", "recall", "F1", "support"]) - return result_obj - - -def print_results(result_obj): - print("Processed %s tokens." % result_obj["processed_tokens"]) - print("Phrases: total=%s, found=%s, correct=%s" % ( - result_obj["total"], - result_obj["found"], - result_obj["correct"] - )) - print(("Overall accuracy: %.2f" % result_obj["overall_accuracy"])) - print(result_obj["prfs"]) - -def get_conll_eval(filename): - cmd = "cat \"%s\" | tr '\\t' ' ' | perl -ne '{chomp;s/\\r//g;print $_,\"\\n\";}' | python NoisyNLP/conlleval.py" % filename - print("Running:\n%s" % cmd) - results = subprocess.check_output(cmd, shell=True) - return parse_results(results) - - - - -def print_regex_matches_all(sentences): - other_entities = { - k: [] for k in OTHER_ENTITIES_KEYS - } - - for seq in sentences: - for t in seq: - for k in other_entities.keys(): - if RegexFeatures.PATTERNS[k].match(t): - other_entities[k].append(t) - for k, v in other_entities.iteritems(): - print(k, len(v)) - - -GLOVE_TWEET_MAPPINGS={ - "": "isMention", - "": "isHashtag", - "": "isDigit", - "": "isURL", - "": "isAllCapitalWord", -} - -def process_glovevectors(filename): - words, dim, errors = 0, 0, 0 - with open(filename, encoding="utf-8", errors="ignore") as fp: - while True: - try: - line = next(fp) - if dim == 0: - dim = len(line.split(" ")) - 1 - except UnicodeDecodeError: - errors += 1 - continue - except StopIteration: - break - if len(line.split(" ")) != dim + 1: - errors += 1 - continue - words+= 1 - print("Words: {}, dim: {}, errors: {}".format(words, dim, errors)) - with open(filename, encoding="utf-8", errors="ignore") as fp, open("{}.processed.txt".format(filename), "w") as fp1: - fp1.write(str(words) + u" " + str(dim) + u"\n") - while True: - try: - line = next(fp) - line = line.strip().split(" ", 1) - line[0] = dict.get(GLOVE_TWEET_MAPPINGS, line[0], line[0]) - out_line = line[0] + u" " + line[1] + u"\n" - if len(out_line.split(" ")) == dim + 1: - fp1.write(out_line) - except (UnicodeDecodeError, UnicodeEncodeError): - pass - except StopIteration: - break - print("Done") - - - -def classification_report_to_df(report): - report_list = [] - for i, line in enumerate(report.split("\n")): - if i == 0: - report_list.append(["class", "precision", "recall", "f1-score", "support"]) - else: - line = line.strip() - if line: - if line.startswith("avg"): - line = line.replace("avg / total", "avg/total") - line = re.split(r'\s+', line) - report_list.append(tuple(line)) - return pd.DataFrame(report_list[1:], columns=report_list[0]) diff --git a/name_entity_recognition/writeToS3.py b/name_entity_recognition/writeToS3.py deleted file mode 100644 index 4fba54f..0000000 --- a/name_entity_recognition/writeToS3.py +++ /dev/null @@ -1,77 +0,0 @@ -import boto3 -import mimetypes -import os -from botocore.client import Config - -client = boto3.client('s3', endpoint_url='http://' + os.environ['HOST_IP'] + ':9000', - aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET'], - config=Config(signature_version='s3v4')) - -bucket_name = os.environ['BUCKET_NAME'] - -def upload(localpath, remotepath, filename): - content_type = mimetypes.guess_type(os.path.join(localpath,filename))[0] - print(filename, content_type) - if content_type == None: - extra_args = {'ContentType':'application/octet-stream'} - else: - extra_args = {'ContentType':content_type} - - client.upload_file(os.path.join(localpath, filename), - bucket_name, - os.path.join(remotepath, filename), - ExtraArgs=extra_args) - - -def createDirectory(DirectoryName): - client.put_object(Bucket=bucket_name, Key=DirectoryName) - - -def generate_downloads(remotepath, filename): - url = client.generate_presigned_url( - ClientMethod='get_object', - Params={ - 'Bucket': bucket_name, - 'Key': os.path.join(remotepath, filename) - }, - ExpiresIn=604800 # one week - ) - - return url - - -def downloadToDisk(filename, localpath, remotepath): - with open(os.path.join(localpath, filename), 'wb') as f: - client.download_fileobj(bucket_name, - os.path.join(remotepath, filename), f) - - -def getObject(remoteKey): - obj = client.get_object(Bucket=bucket_name, Key=remoteKey) - - -def putObject(body, remoteKey): - # bytes or seekable file-like object - obj = client.put_object(Bucket=bucket_name, - Body=body, Key=remoteKey) - print(obj['Body'].read()) - -def listDir(remoteClass): - objects = client.list_objects(Bucket=bucket_name, - Prefix=remoteClass, - Delimiter='/') - foldernames = [] - for o in objects.get('CommonPrefixes'): - foldernames.append(o.get('Prefix')) - - # only return the list of foldernames - return foldernames - - -def listFiles(foldernames): - objects = client.list_objects(Bucket=bucket_name, - Prefix=foldernames) - - # return rich information about the files - return objects.get('Contents') diff --git a/name_entity_recognition_extractor/CHANGELOG.md b/name_entity_recognition_extractor/CHANGELOG.md new file mode 100644 index 0000000..332c0e2 --- /dev/null +++ b/name_entity_recognition_extractor/CHANGELOG.md @@ -0,0 +1,16 @@ +# Changelog +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.1.0] - 03-15-2023 + +### Added +- Initial release of the name entity recognition extractor + + +## [0.1.1] - 10-03-2023 + +### Changed +- Support Clowder V2 [#1](https://github.com/clowder-framework/smm-extractor/issues/1) diff --git a/name_entity_recognition/data/wikidata/queries/music_artists.txt.json b/name_entity_recognition_extractor/README.md similarity index 100% rename from name_entity_recognition/data/wikidata/queries/music_artists.txt.json rename to name_entity_recognition_extractor/README.md diff --git a/preprocessing/SmmExtractor.py b/name_entity_recognition_extractor/SmmExtractor.py similarity index 95% rename from preprocessing/SmmExtractor.py rename to name_entity_recognition_extractor/SmmExtractor.py index 6aefd2c..d79adf2 100644 --- a/preprocessing/SmmExtractor.py +++ b/name_entity_recognition_extractor/SmmExtractor.py @@ -103,6 +103,7 @@ def process_message(self, connector, host, secret_key, resource, parameters): # execute the algorithm # Parse user parameters to determine which column to analyze userParams = parameters.get('parameters') + output = algorithm(df, userParams) connector.message_process(resource, "Running the algorithm...") @@ -110,14 +111,15 @@ def process_message(self, connector, host, secret_key, resource, parameters): for fname, output_data in output.items(): if fname != 'uid': local_output_path = save_local_output("", fname, output_data) - uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, - local_output_path) connector.message_process(resource, "Saving " + local_output_path + "...") + uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, + local_output_path) + connector.message_process(resource, local_output_path + " saved...") - # write params to metadata + connector.message_process(resource, "Writing metadata...") metadata = self.get_metadata(userParams, 'file', uploaded_file_id, host) pyclowder.files.upload_metadata(connector, host, secret_key, uploaded_file_id, metadata) - connector.message_process(resource, "Writing metadata...") + connector.message_process(resource, "Metadata written...") if __name__ == "__main__": diff --git a/name_entity_recognition_extractor/extractor.dockerfile b/name_entity_recognition_extractor/extractor.dockerfile new file mode 100644 index 0000000..35ff919 --- /dev/null +++ b/name_entity_recognition_extractor/extractor.dockerfile @@ -0,0 +1,19 @@ +FROM socialmediamacroscope/name_entity_recognition:latest + + +RUN mkdir -p /scripts +WORKDIR /scripts + +COPY ./SmmExtractor.py ./ +COPY ./extractor_info.json ./ +COPY ./requirement.txt ./extractor-requirement.txt + +# Install pyClowder and any other python dependencies +RUN pip3 install --no-cache-dir -r ./extractor-requirement.txt -U + +# Command to be run when container is run +# Can add heartbeat to change the refresh rate +CMD python3 SmmExtractor.py --heartbeat 300 + +ENV MAIN_SCRIPT="SmmExtractor.py" \ + CLOWDER_VERSION=1 diff --git a/name_entity_recognition/extractor_info.json b/name_entity_recognition_extractor/extractor_info.json similarity index 53% rename from name_entity_recognition/extractor_info.json rename to name_entity_recognition_extractor/extractor_info.json index 5a7794f..b700c56 100644 --- a/name_entity_recognition/extractor_info.json +++ b/name_entity_recognition_extractor/extractor_info.json @@ -1,15 +1,19 @@ { "@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", - "name": "smm.name-entity.recognition", - "version": "0.1", - "description": "Description on this extractor", + "name": "smm.name.entity.recognition", + "version": "0.1.1", + "description": "Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.", "author": "Wang, Chen ", "contributors": [], "contexts": [{}], "repository": [ { "repType": "git", - "repUrl": "https://github.com/ncsa/standalone-smm-analytics" + "repUrl": "https://github.com/ncsa/standalone-smm-analytics.git" + }, + { + "repType": "git", + "repUrl": "https://github.com/clowder-framework/smm-extractor.git" } ], "process": { diff --git a/name_entity_recognition_extractor/requirement.txt b/name_entity_recognition_extractor/requirement.txt new file mode 100644 index 0000000..74e79f5 --- /dev/null +++ b/name_entity_recognition_extractor/requirement.txt @@ -0,0 +1,2 @@ +pyclowder==3.0.4 + diff --git a/network_analysis/algorithm.py b/network_analysis/algorithm.py deleted file mode 100644 index 54753ae..0000000 --- a/network_analysis/algorithm.py +++ /dev/null @@ -1,35 +0,0 @@ -import plot -from network_analysis import Network - - -def algorithm(df, params): - """ - wrapper function to put each individual algorithm inside - :param df: dataframe that contains all the input dataset - :param params: algorithm specific parameters - :return: a dictionary of { outputname: output content in memory } - """ - - output = {} - - # algorithm specific code - # construct network analysis - NW = Network(df, params['relations']) - output['d3js'] = NW.export_json() - output['gephi'] = NW.export_gephi() - output['pajek'] = NW.export_pajek() - output['assortativity'] = NW.assortativity() - output['node_attributes'] = NW.node_attributes() - output['edge_attributes'] = NW.edge_attributes() - output['strong_components'] = NW.strong_components() - output['weak_components'] = NW.weak_components() - output['triads'] = NW.triads() - - # plot network - pruned_network = NW.prune_network() - output['div'] = plot.plot_network(pruned_network, params['layout'], - params['relations'], - title=params['relations'] - + ' Network graph of 500 nodes with highest degree centrality') - - return output diff --git a/network_analysis/batch_function.py b/network_analysis/batch_function.py deleted file mode 100644 index 63ba1ea..0000000 --- a/network_analysis/batch_function.py +++ /dev/null @@ -1,58 +0,0 @@ -import dataset -import argparse -from notification import notification -from algorithm import algorithm - -if __name__ == '__main__': - - # entrance to invoke Batch - urls = {} - - # default parameters - parser = argparse.ArgumentParser(description="processing...") - parser.add_argument('--remoteReadPath', required=True) - parser.add_argument('--column', required=True) - parser.add_argument('--s3FolderName', required=True) - parser.add_argument('--uid', required=True) - parser.add_argument('--resultPath', required=True) - parser.add_argument('--email', required=True) - parser.add_argument('--sessionURL', required=True) - - # user specified parameters - parsed, unknown = parser.parse_known_args() - for arg in unknown: - if arg.startswith("--"): - parser.add_argument(arg, required=False) - - params = vars(parser.parse_args()) - - # arranging the paths - path = dataset.organize_path_lambda(params) - - # save the config file - urls['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - urls[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - urls[key] = value - - # push notification email - notification(toaddr=params['email'], case=3, filename=path['remoteSavePath'], - links=urls, sessionURL=params['sessionURL']) diff --git a/network_analysis/clear_cache.sh b/network_analysis/clear_cache.sh deleted file mode 100644 index 640c2ac..0000000 --- a/network_analysis/clear_cache.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env bash -CACHEDIR=/tmp - -# delete file older than 4 hours -find "$CACHEDIR" -maxdepth 1 -mindepth 1 -type d -mmin +240 -print0 | - while IFS= read -d '' -r dir; do - rm -rf "$dir" - done \ No newline at end of file diff --git a/network_analysis/clear_cache_cron b/network_analysis/clear_cache_cron deleted file mode 100644 index 0510c51..0000000 --- a/network_analysis/clear_cache_cron +++ /dev/null @@ -1 +0,0 @@ -0 * * * * /bin/bash /scripts/clear_cache.sh diff --git a/network_analysis/command.txt b/network_analysis/command.txt deleted file mode 100644 index e037d8b..0000000 --- a/network_analysis/command.txt +++ /dev/null @@ -1,4 +0,0 @@ -# command to build and run this container -docker build -t socialmediamacroscope/network_analysis:latest . -docker build -f extractor.dockerfile -t socialmediamacroscope/network_analysis_extractor:latest . -docker push socialmediamacroscope/network_analysis_extractor:latest diff --git a/network_analysis/dataset.py b/network_analysis/dataset.py deleted file mode 100644 index c1bb7b5..0000000 --- a/network_analysis/dataset.py +++ /dev/null @@ -1,148 +0,0 @@ -import csv -import os -import json -import pickle -import pandas as pd -import types -import writeToS3 as s3 - - -def organize_path_lambda(event): - """ - parse the lambda handler parameter event to construct necessary paths for reading and storing data - :param event: aws lambda parameters from handler - :return: path dictionary - """ - # arranging the paths - localReadPath = os.path.join('/tmp', event['s3FolderName'], event['uid']) - localSavePath = os.path.join('/tmp', - event['s3FolderName'] + event['resultPath'], - event['uid']) - remoteSavePath = os.path.join(event['s3FolderName'] + event['resultPath'], - event['uid']) - if 'remoteReadPath' not in event.keys() or event['remoteReadPath'] == 'undefined': - remoteReadPath = remoteSavePath - filename = event['labeledFilename'] - else: - remoteReadPath = event['remoteReadPath'] - filename = remoteReadPath.split('/')[-2] + '.csv' - - if not os.path.exists(localSavePath): - os.makedirs(localSavePath) - if not os.path.exists(localReadPath): - os.makedirs(localReadPath) - - path = { - 'remoteReadPath': remoteReadPath, - 'localReadPath': localReadPath, - 'localSavePath': localSavePath, - 'remoteSavePath': remoteSavePath, - 'filename': filename - } - - return path - - -def get_remote_input(remoteReadPath, filename, localReadPath): - """ - download input file from s3 bucket to a local location, and then load - it to a pandas dataframe - :param remoteReadPath: remote path in s3 to store the data - :param localReadPath: local location to store the data, usually in /tmp - :return: df: dataframe that contains the complete input file - """ - s3.downloadToDisk(filename, localReadPath, remoteReadPath) - - # quick fix for decoding error, sometimes the data is coded in ISO-8859-1 - # Array = 2D nested list holding column and row data - Array = [] - try: - with open(os.path.join(localReadPath, filename), 'r', - encoding='utf-8', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - except Exception: - with open(os.path.join(localReadPath, filename), 'r', - encoding='ISO-8859-1', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - - # load to pandas dataframe - df = pd.DataFrame(Array[1:], columns=Array[0]) - - return df - - -def save_remote_output(localSavePath, remoteSavePath, fname, output_data): - """ - save output in memory first to local file, then upload to remote S3 bucket - :param localSavePath: local saved file - :param remoteSavePath: remote save file path - :param fname: filename - :param output_data: the actual data - :return: url of the file saved in S3 bucket - """ - - # json - if isinstance(output_data, dict): - fname += '.json' - with open(os.path.join(localSavePath, fname), 'w') as f: - json.dump(output_data, f) - - # dataframe to csv - elif isinstance(output_data, pd.DataFrame): - fname += '.csv' - output_data.to_csv(fname, encoding='utf-8') - - # string to html - elif isinstance(output_data, str): - fname += '.html' - with open(os.path.join(localSavePath, fname), 'w') as f: - f.write(output_data) - - # list(list) to csv - elif isinstance(output_data, list) \ - and (isinstance(output_data[0], list) or isinstance(output_data[0], - tuple)): - fname += '.csv' - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - writer = csv.writer(f) - for row in output_data: - try: - writer.writerow(row) - except UnicodeEncodeError as e: - print(e) - - # special case - elif isinstance(output_data, types.GeneratorType): - if fname == 'gephi': - fname += '.gml' - elif fname == 'pajek': - fname += '.net' - else: - fname += '.unknown' - - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - for line in output_data: - f.write(line + '\n') - - # else pickle the object - else: - fname += '.pickle' - with open(os.path.join(localSavePath, fname), 'wb') as f: - pickle.dump(output_data, f) - - s3.upload(localSavePath, remoteSavePath, fname) - url = s3.generate_downloads(remoteSavePath, fname) - - return url diff --git a/network_analysis/dockerfile b/network_analysis/dockerfile deleted file mode 100644 index b0fb03e..0000000 --- a/network_analysis/dockerfile +++ /dev/null @@ -1,15 +0,0 @@ -FROM python:3.7 - -RUN apt-get -qq -y update && apt-get -qq -y install cron - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -RUN pip install --no-cache-dir -r requirement.txt - -# cron job clean tmp folder -RUN chmod u+x ./clear_cache.sh -RUN chmod 0644 ./clear_cache_cron -RUN crontab ./clear_cache_cron diff --git a/network_analysis/extractor.dockerfile b/network_analysis/extractor.dockerfile deleted file mode 100644 index f99c472..0000000 --- a/network_analysis/extractor.dockerfile +++ /dev/null @@ -1,15 +0,0 @@ -FROM python:3.8.5 - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -RUN pip install --no-cache-dir -r requirement.txt - - -# Command to be run when container is run -# Can add heartbeat to change the refresh rate -CMD python3 SmmExtractor.py --heartbeat 40 - -ENV MAIN_SCRIPT="SmmExtractor.py" diff --git a/network_analysis/network_analysis.py b/network_analysis/network_analysis.py deleted file mode 100755 index fdb3150..0000000 --- a/network_analysis/network_analysis.py +++ /dev/null @@ -1,239 +0,0 @@ -import networkx as nx -from networkx.readwrite import json_graph -import pandas - - -def extract_relation_graph(df, relation, text_field, username_field, id_column=None): - if id_column: - df = df[[id_column, text_field, username_field]].dropna() - if relation == 'retweet_from': - df[relation] = df[text_field].str.extract( - 'RT @([A-Za-z0-9-_]+):', expand=True) - new_df = df[ - [id_column, relation, username_field, text_field]].dropna() - elif relation == 'reply_to': - df[relation] = df[relation] = df[text_field].str.extract( - '^@([A-Za-z0-9-_]+)', expand=True) - new_df = df[[id_column, relation, username_field, text_field]].dropna() - elif relation == 'mentions': - df[relation] = df[text_field].str.findall('@([A-Za-z0-9-_]+)') - tmp = [] - - def __backend(r): - x = r[username_field] - y = r[text_field] - i = r[id_column] - zz = r[relation] - for z in zz: - tmp.append({username_field: x, - text_field: y, - id_column: i, - relation: z}) - - df.apply(__backend, axis=1) - new_df = pandas.DataFrame(tmp).dropna() - else: - raise ValueError( - 'The relation you select is not implemented in this analysis yet.') - - graph = nx.DiGraph() - for row in new_df.iterrows(): - graph.add_edge(row[1][username_field], - row[1][relation], - text=row[1][text_field], - id = row[1][id_column]) - return graph - - else: - df = df[[text_field, username_field]].dropna() - if relation == 'retweet_from': - df[relation] = df[text_field].str.extract( - 'RT @([A-Za-z0-9-_]+):', expand=True) - new_df = df[ - [relation, username_field, text_field]].dropna() - elif relation == 'reply_to': - df[relation] = df[relation] = df[text_field].str.extract( - '^@([A-Za-z0-9-_]+)', expand=True) - new_df = df[[relation, username_field, text_field]].dropna() - elif relation == 'mentions': - df[relation] = df[text_field].str.findall('@([A-Za-z0-9-_]+)') - tmp = [] - - def __backend(r): - x = r[username_field] - y = r[text_field] - zz = r[relation] - for z in zz: - tmp.append({username_field: x, - text_field: y, - relation: z}) - - df.apply(__backend, axis=1) - new_df = pandas.DataFrame(tmp).dropna() - else: - raise ValueError( - 'The relation you select is not implemented in this analysis yet.') - - graph = nx.DiGraph() - for row in new_df.iterrows(): - graph.add_edge(row[1][username_field], - row[1][relation], - text=row[1][text_field]) - return graph - - -class Network: - - def __init__(self, df, relationships): - # construct network - # 3 networks: reply to, retweet from, and mentions - # 2 datasources with different field: twitter-Tweet & twitter-Stream - - columns = df.columns.values.tolist() - if 'text' in columns and 'user.screen_name' in columns: - if 'id_str' in columns: - self.graph = extract_relation_graph(df, relationships, 'text', - 'user.screen_name', - id_column='id_str') - else: - self.graph = extract_relation_graph(df, relationships, 'text', - 'user.screen_name') - elif '_source.text' in columns and '_source.user.screen_name' in columns: - if '_source.id_str' in columns: - self.graph = extract_relation_graph(df, relationships, - '_source.text', - '_source.user.screen_name', - id_column='_source.id_str') - else: - self.graph = extract_relation_graph(df, relationships, - '_source.text', - '_source.user.screen_name') - else: - raise ValueError( - 'The File you selected does not have complete ' - 'information to construct a network. You must ' - 'have the full tweet as well as the author information.') - - def prune_network(self): - d = nx.degree_centrality(self.graph) - d = sorted(d.items(), key=lambda x: x[1], reverse=True) - if len(d) >= 500: - for n in d[500:]: - self.graph.remove_node(n[0]) - self.graph.remove_nodes_from(nx.isolates(self.graph)) - - return self.graph - - def export_json(self): - d3js_graph = json_graph.node_link_data(self.graph) - d3js_graph['nodes'] = [{'id': node['id'], - 'connectivity': self.graph.in_degree()[ - node['id']] - + self.graph.out_degree()[ - node['id']]} - for node in d3js_graph['nodes']] - - return d3js_graph - - def export_gephi(self): - return nx.generate_gml(self.graph) - - def export_pajek(self): - return nx.generate_pajek(self.graph) - - def assortativity(self): - result = {} - result['average_degree_connectivity'] = nx.average_degree_connectivity( - self.graph) - result['k_nearest_neighbors'] = nx.k_nearest_neighbors(self.graph) - - # k degree distribution - k_degree = [] - for k in result['average_degree_connectivity'].keys(): - k_degree.append((k, result['average_degree_connectivity'][k], - result['k_nearest_neighbors'][k])) - - k_degree.insert(0, ['degree k', 'average_degree_connectivity', - 'k_nearest_neighbors']) - - return k_degree - - def node_attributes(self): - result = {} - - result['degree_centrality'] = nx.degree_centrality(self.graph) - result['in_degree_centrality'] = nx.in_degree_centrality(self.graph) - result['out_degree_centrality'] = nx.out_degree_centrality(self.graph) - result['closeness_centrality'] = nx.closeness_centrality(self.graph) - result['betweenness_centrality'] = nx.betweenness_centrality( - self.graph) - result['load_centrality'] = nx.load_centrality(self.graph) - result['average_neighbor_degree'] = nx.average_neighbor_degree( - self.graph) - result['square_clustering'] = nx.square_clustering(self.graph) - result['closeness_vitality'] = nx.closeness_vitality(self.graph) - - # nodes attributes - node_attributes = [] - for node in self.graph.nodes(): - node_attributes.append((node, result['degree_centrality'][node], - result['in_degree_centrality'][node], - result['out_degree_centrality'][node], - result['closeness_centrality'][node], - result['betweenness_centrality'][node], - result['load_centrality'][node], - result['average_neighbor_degree'][node], - result['square_clustering'][node], - result['closeness_vitality'][node])) - - node_attributes.insert(0, ['node', - 'degree_centrality', - 'in_degree_centrality', - 'out_degree_centrality', - 'closeness_centrality', - 'betweenness_centrality', - 'load_centrality', - 'average_neighbor_degree', - 'square_clustering', - 'closeness_vitality']) - - return node_attributes - - def edge_attributes(self): - result = {} - result['edge_betweenness_centrality'] = nx.edge_betweenness_centrality( - self.graph) - result['edge_load'] = nx.edge_load(self.graph) - - edge_attributes = [] - for edge in self.graph.edges(): - edge_attributes.append((edge, - result['edge_betweenness_centrality'][ - edge], - result['edge_load'][edge])) - edge_attributes.insert(0, ['edge', 'edge_betweenness_centrality', - 'edge_load']) - - return edge_attributes - - def strong_components(self): - strong = nx.strongly_connected_components(self.graph) - strong_nodes = [['strongly_connected_component']] - for n in strong: - strong_nodes.append([list(n)[0]]) - - return strong_nodes - - def weak_components(self): - weak = nx.weakly_connected_components(self.graph) - weak_nodes = [['weakly_connected_component']] - for n in weak: - weak_nodes.append([list(n)[0]]) - - return weak_nodes - - def triads(self): - rslt = {} - rslt['triadic_census'] = nx.triadic_census(self.graph) - - return rslt diff --git a/network_analysis/notification.py b/network_analysis/notification.py deleted file mode 100644 index 176e095..0000000 --- a/network_analysis/notification.py +++ /dev/null @@ -1,170 +0,0 @@ -import smtplib -from email.mime.multipart import MIMEMultipart -from email.mime.text import MIMEText -import os - - -def notification(toaddr,case,filename,links,sessionURL): - # toaddr -- email address to send to - # text content to send - # subject - host = os.environ.get('EMAIL_HOST') - port = os.environ.get('EMAIL_PORT') - fromaddr = os.environ.get('EMAIL_FROM_ADDRESS') - password = os.environ.get('EMAIL_PASSWORD') - - if host is not None and host != "" and \ - port is not None and port != "" and\ - fromaddr is not None and fromaddr != "" and \ - password is not None and password != "": - # map the fpath component to History panel names - # local/NLP/sentiment/xxxxxxxxxxxxxxxxxxxxxxxx/ => [local,nlp,sentiment,xxxx,space] - # [local, GraphQL, reddit-post, aww, space] - # 0 1 2 3 - if filename != '': - fpath = filename.split('/') - - if fpath[1] == 'GraphQL': - fpath[1] = 'Social Media Data' - elif fpath[1] == 'NLP': - fpath[1] = 'Nature Language Processing' - elif fpath[1] == 'ML': - fpath[1] = 'Machine Learning ML' - elif fpath[1] == 'NW': - fpath[1] = 'Network Visualization and Analysis' - - if fpath[2] == 'reddit-Post': - fpath[2] = 'Subreddit Posts Title' - elif fpath[2] == 'reddit-Historical-Post': - fpath[2] = 'Reddit Historical Post' - elif fpath[2] == 'reddit-Search': - fpath[2] = 'Reddit Search Posts Title' - elif fpath[2] == 'sentiment': - fpath[2] = 'Sentiment Analysis' - elif fpath[2] == 'preprocessing': - fpath[2] = 'NLP Preprocessing' - elif fpath[2] == 'networkx': - fpath[2] = 'Python NetworkX' - elif fpath[2] == 'classification': - fpath[2] = 'Text Classification' - - if case == 0 or case == 'comment-fail': - html = """ - - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection has been terminated.

      -

      We are using the id and permalink from your Reddit Submission dataset - to collect comments and replies. It is most likely you have provide an incomplete Reddit Submission dataset missing these two fields.

      -

      Please try to reproduce the Reddit Submission with id and permalink, or switch to another dataset.

      - Go to your session... -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - - """ - subject = 'Your Reddit Comment collection has failed...' - elif case == 1 or case == 'comment-terminate': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is exceeding 400 Megabyte, and is terminated due to lack of disk space.

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. The partial comments we manage to collect and save will be compressed for you in an .zip file named """ + fpath[3] + """-comments.zip (click)
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
          -
          -

          Best Regards,

          -

          Social Media Macroscope - SMILE

          -
      - - """ - subject = 'Your Reddit Comment collection has been terminated...' - elif case == 2 or case == 'comment-success': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is ready for you!

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. It will be compressed for you in an .zip file named """+ fpath[3] +"""-comments.zip
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] +"""
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your Reddit Comment collection is completed!' - elif case == 3 or case == 'analytics-success': - list_html = '' - for key in links.keys(): - list_html += '
    • ' + key + '
      • ' - - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your """ + fpath[2] + """ results are ready for you! (job ID: """ + fpath[3] + """)

      -
        -
      • You can view the visualization and download the results at HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """ tab
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click view
          • -
        -
        -
      • You can also click the link below to download part of the results: -
          """ + list_html + """
        -
        • -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your ' + fpath[2] + ' computation is completed!' - - msg = MIMEMultipart('alternative') - msg['Subject'] = subject - msg['From'] = fromaddr - msg['To'] = toaddr - msg.attach(MIMEText(html, 'html')) - - server = smtplib.SMTP_SSL(host, port) - server.login(fromaddr, password) - server.sendmail(fromaddr, toaddr, msg.as_string()) - server.quit() - else: - print("Invalid Email host setting! Skip notification.") diff --git a/network_analysis/plot.py b/network_analysis/plot.py deleted file mode 100644 index 431d2ff..0000000 --- a/network_analysis/plot.py +++ /dev/null @@ -1,157 +0,0 @@ -from plotly.graph_objs import * -from plotly.offline import plot -import networkx as nx - - -def plot_pie_chart(labels, values, title): - """ - plot pie chart - :param labels: list of label, shape must match parameter values - :param values: list of values, shape must match parameter labels - :param title: title to show - :return: html code in a div - """ - trace = Pie(labels=labels, values=values, textinfo='label+percent') - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_network(graph, layout, relationships, title): - """ - plot network graph - :param graph: networkx graph - :param layout: network layout - :param relationships: reply, retweet, mention or anything else - :param title: title to show - :return: html code in a div - """ - - if layout == 'spring': - pos = nx.spring_layout(graph) - elif layout == 'circular': - pos = nx.circular_layout(graph) - elif layout == 'fruchterman': - pos = nx.fruchterman_reingold_layout(graph) - elif layout == 'random': - pos = nx.random_layout(graph) - elif layout == 'shell': - pos = nx.shell_layout(graph) - elif layout == 'spectral': - pos = nx.spectral_layout(graph) - edge_attri = nx.get_edge_attributes(graph, 'text') - edge_trace = Scatter(x=[], y=[], text=[], - line=Line(width=1, color='#b5b5b5'), - hoverinfo='text', - mode='lines', - hoveron='points') - for edge in graph.edges(): - x0, y0 = pos[edge[0]] - x1, y1 = pos[edge[1]] - edge_trace['x'] += [x0, x1, None] - edge_trace['y'] += [y0, y1, None] - edge_trace['text'].append(edge_attri[edge]) - - node_trace = Scatter(x=[], y=[], text=[], mode='markers', - hoverinfo='text', hoveron='points+fills', - marker=Marker(showscale=True, - colorscale='Portland', - reversescale=False, - color=[], - size=10, - colorbar=dict(thickness=15, - title='node in-degree plus out-degree', - xanchor='left', - titleside='right'), - line=dict(width=2))) - for node in graph.nodes(): - x, y = pos[node] - node_trace['x'].append(x) - node_trace['y'].append(y) - - # set label - for node in graph.nodes(): - node_trace['marker']['color'].append(graph.in_degree()[node] + graph.out_degree()[node]) - if relationships == 'reply_to': - node_trace['text'].append("@" + node + " is replied by " - + str(graph.in_degree()[node]) - + " user(s), and replies to " - + str(graph.out_degree()[node]) + " user(s)") - - elif relationships == 'retweet_from': - node_trace['text'].append("@" + node + " is retweeted by " - + str(graph.in_degree()[node]) - + " user(s) and retweets from " - + str(graph.out_degree()[node]) - + " user(s)") - - elif relationships == 'mentions': - node_trace['text'].append("@" + node + " is mentioned by " - + str(graph.in_degree()[node]) - + " user(s) and mentions " - + str(graph.out_degree()[node]) - + " user(s)") - - fig = Figure(data=Data([edge_trace, node_trace]), - layout=Layout( - title=title, - titlefont=dict(size=16), showlegend=False, - hovermode='closest', margin=dict(b=20, l=5, r=5, t=40), - xaxis=XAxis(showgrid=False, zeroline=False, - showticklabels=False), - yaxis=YAxis(showgrid=False, zeroline=False, - showticklabels=False) - )) - - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_bar_chart(index, counts, title): - """ - plot bar chart - :param index: x - axis, usually the index - :param counts: y - axis, usually counts - :param title: - :return: - """ - - trace = Bar(x=index, y=counts, - marker=dict(color='rgba(200,75,73,1.0)', - line=dict(color='rgba(111,11,9,1.0)',width=2))) - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - yaxis=dict( - showgrid=True, - showline=True, - showticklabels=True, - linecolor='rgba(102, 102, 102, 0.8)', - linewidth=2 - ), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, image_filename='plot_img') - - return div diff --git a/network_analysis/postToAWSBatch.py b/network_analysis/postToAWSBatch.py deleted file mode 100644 index 73c93f5..0000000 --- a/network_analysis/postToAWSBatch.py +++ /dev/null @@ -1,24 +0,0 @@ -import boto3 -import os - -client = boto3.client('batch', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(jobDefinition, jobName, jobQueue, command): - command.extend(['--HOST_IP', os.environ['HOST_IP'], - '--AWS_ACCESSKEY', os.environ['AWS_ACCESSKEY'], - '--AWS_ACCESSKEYSECRET', os.environ['AWS_ACCESSKEYSECRET'], - '--BUCKET_NAME', os.environ['BUCKET_NAME']]) - - response = client.submit_job( - jobDefinition=jobDefinition, - jobName=jobName, - jobQueue=jobQueue, - containerOverrides={ - 'vcpus': 2, - 'memory': 2048, - 'command': command - } - ) - - return response \ No newline at end of file diff --git a/network_analysis/postToAWSLambda.py b/network_analysis/postToAWSLambda.py deleted file mode 100644 index 8a227e7..0000000 --- a/network_analysis/postToAWSLambda.py +++ /dev/null @@ -1,28 +0,0 @@ -import json -import boto3 -import os - -client = boto3.client('lambda', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(function_name, args): - - # pass information so remote lambda can access local s3 minio - args['HOST_IP'] = os.environ['HOST_IP'] - args['AWS_ACCESSKEY'] = os.environ['AWS_ACCESSKEY'] - args['AWS_ACCESSKEYSECRET'] = os.environ['AWS_ACCESSKEYSECRET'] - args['BUCKET_NAME'] = os.environ['BUCKET_NAME'] - - response = client.invoke( - Payload=json.dumps(args), - FunctionName=function_name, - InvocationType='RequestResponse', - LogType='Tail', - ) - - if response['StatusCode'] == 200: - results = json.loads(response['Payload'].read().decode('utf-8')) - else: - results = {'ERROR': response['FunctionError']} - - return results \ No newline at end of file diff --git a/network_analysis/rabbitmq_handler.py b/network_analysis/rabbitmq_handler.py deleted file mode 100644 index 247844c..0000000 --- a/network_analysis/rabbitmq_handler.py +++ /dev/null @@ -1,99 +0,0 @@ -import json -import os -import traceback - -import dataset -import pika -from algorithm import algorithm - -import postToAWSLambda -import postToAWSBatch - - -def rabbitmq_handler(ch, method, properties, body): - try: - msg = {} - - # determine if it goes to aws, lambda, or batch - params = json.loads(body) - - if params['platform'] == 'aws-lambda': - msg = postToAWSLambda.invoke(params['function_name'], params) - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - elif params['platform'] == 'lambda': - path = dataset.organize_path_lambda(params) - - # save the config file - msg['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - msg[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - msg[key] = value - - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - elif params['platform'] == 'aws-batch': - postToAWSBatch.invoke(params['jobDefinition'], - params['jobName'], - params['jobQueue'], - params['command']) - - elif params['platform'] == 'batch': - os.system(' '.join(params['command'])) - - else: - raise ValueError( - 'Rabbitmq Message Not Recognizable. ' - 'It has to specify what platform to run: aws-lambda, aws-batch, lambda or batch.') - - except BaseException as e: - - msg = {'ERROR': - {'message': str(e), - 'traceback': traceback.format_exc() - } - } - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - return None - - -if __name__ == '__main__': - connection = pika.BlockingConnection(pika.ConnectionParameters(port=5672, host="rabbitmq")) - channel = connection.channel() - - # pass the queue name in environment variable - queue = os.environ['QUEUE_NAME'] - - channel.queue_declare(queue=queue) - channel.basic_qos(prefetch_count=1) - channel.basic_consume(queue=queue, on_message_callback=rabbitmq_handler, auto_ack=True) - channel.start_consuming() diff --git a/network_analysis/requirement.txt b/network_analysis/requirement.txt deleted file mode 100644 index 6dd8636..0000000 --- a/network_analysis/requirement.txt +++ /dev/null @@ -1,7 +0,0 @@ -boto3>=1.6.11 -numpy>=1.16.1 -pandas>=0.24.1 -networkx==1.11 -plotly==2.7.0 -pika>=1.1.0 -pyclowder==2.7.0 diff --git a/network_analysis/writeToS3.py b/network_analysis/writeToS3.py deleted file mode 100644 index 4fba54f..0000000 --- a/network_analysis/writeToS3.py +++ /dev/null @@ -1,77 +0,0 @@ -import boto3 -import mimetypes -import os -from botocore.client import Config - -client = boto3.client('s3', endpoint_url='http://' + os.environ['HOST_IP'] + ':9000', - aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET'], - config=Config(signature_version='s3v4')) - -bucket_name = os.environ['BUCKET_NAME'] - -def upload(localpath, remotepath, filename): - content_type = mimetypes.guess_type(os.path.join(localpath,filename))[0] - print(filename, content_type) - if content_type == None: - extra_args = {'ContentType':'application/octet-stream'} - else: - extra_args = {'ContentType':content_type} - - client.upload_file(os.path.join(localpath, filename), - bucket_name, - os.path.join(remotepath, filename), - ExtraArgs=extra_args) - - -def createDirectory(DirectoryName): - client.put_object(Bucket=bucket_name, Key=DirectoryName) - - -def generate_downloads(remotepath, filename): - url = client.generate_presigned_url( - ClientMethod='get_object', - Params={ - 'Bucket': bucket_name, - 'Key': os.path.join(remotepath, filename) - }, - ExpiresIn=604800 # one week - ) - - return url - - -def downloadToDisk(filename, localpath, remotepath): - with open(os.path.join(localpath, filename), 'wb') as f: - client.download_fileobj(bucket_name, - os.path.join(remotepath, filename), f) - - -def getObject(remoteKey): - obj = client.get_object(Bucket=bucket_name, Key=remoteKey) - - -def putObject(body, remoteKey): - # bytes or seekable file-like object - obj = client.put_object(Bucket=bucket_name, - Body=body, Key=remoteKey) - print(obj['Body'].read()) - -def listDir(remoteClass): - objects = client.list_objects(Bucket=bucket_name, - Prefix=remoteClass, - Delimiter='/') - foldernames = [] - for o in objects.get('CommonPrefixes'): - foldernames.append(o.get('Prefix')) - - # only return the list of foldernames - return foldernames - - -def listFiles(foldernames): - objects = client.list_objects(Bucket=bucket_name, - Prefix=foldernames) - - # return rich information about the files - return objects.get('Contents') diff --git a/network_analysis_extractor/CHANGELOG.md b/network_analysis_extractor/CHANGELOG.md new file mode 100644 index 0000000..2136e0f --- /dev/null +++ b/network_analysis_extractor/CHANGELOG.md @@ -0,0 +1,16 @@ +# Changelog +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.1.0] - 03-15-2023 + +### Added +- Initial release of the network analysis extractor + + +## [0.1.1] - 10-03-2023 + +### Changed +- Support Clowder V2 [#1](https://github.com/clowder-framework/smm-extractor/issues/1) diff --git a/network_analysis_extractor/README.md b/network_analysis_extractor/README.md new file mode 100644 index 0000000..e69de29 diff --git a/name_entity_recognition/SmmExtractor.py b/network_analysis_extractor/SmmExtractor.py similarity index 95% rename from name_entity_recognition/SmmExtractor.py rename to network_analysis_extractor/SmmExtractor.py index 6aefd2c..d79adf2 100644 --- a/name_entity_recognition/SmmExtractor.py +++ b/network_analysis_extractor/SmmExtractor.py @@ -103,6 +103,7 @@ def process_message(self, connector, host, secret_key, resource, parameters): # execute the algorithm # Parse user parameters to determine which column to analyze userParams = parameters.get('parameters') + output = algorithm(df, userParams) connector.message_process(resource, "Running the algorithm...") @@ -110,14 +111,15 @@ def process_message(self, connector, host, secret_key, resource, parameters): for fname, output_data in output.items(): if fname != 'uid': local_output_path = save_local_output("", fname, output_data) - uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, - local_output_path) connector.message_process(resource, "Saving " + local_output_path + "...") + uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, + local_output_path) + connector.message_process(resource, local_output_path + " saved...") - # write params to metadata + connector.message_process(resource, "Writing metadata...") metadata = self.get_metadata(userParams, 'file', uploaded_file_id, host) pyclowder.files.upload_metadata(connector, host, secret_key, uploaded_file_id, metadata) - connector.message_process(resource, "Writing metadata...") + connector.message_process(resource, "Metadata written...") if __name__ == "__main__": diff --git a/network_analysis_extractor/extractor.dockerfile b/network_analysis_extractor/extractor.dockerfile new file mode 100644 index 0000000..6fe3071 --- /dev/null +++ b/network_analysis_extractor/extractor.dockerfile @@ -0,0 +1,18 @@ +FROM socialmediamacroscope/network_analysis:latest + +RUN mkdir -p /scripts +WORKDIR /scripts + +COPY ./SmmExtractor.py ./ +COPY ./extractor_info.json ./ +COPY ./requirement.txt ./extractor-requirement.txt + +# Install pyClowder and any other python dependencies +RUN pip3 install --no-cache-dir -r ./extractor-requirement.txt -U + +# Command to be run when container is run +# Can add heartbeat to change the refresh rate +CMD python3 SmmExtractor.py --heartbeat 300 + +ENV MAIN_SCRIPT="SmmExtractor.py" \ + CLOWDER_VERSION=1 diff --git a/network_analysis/extractor_info.json b/network_analysis_extractor/extractor_info.json similarity index 71% rename from network_analysis/extractor_info.json rename to network_analysis_extractor/extractor_info.json index 6467cdd..ff015dc 100644 --- a/network_analysis/extractor_info.json +++ b/network_analysis_extractor/extractor_info.json @@ -1,15 +1,19 @@ { "@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", "name": "smm.network.analysis", - "version": "0.1", - "description": "Description on this extractor", + "version": "0.1.1", + "description": "Social network analysis is the process of investigating social structures through the use of networks and graph theory .It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.", "author": "Wang, Chen ", "contributors": [], "contexts": [{}], "repository": [ { "repType": "git", - "repUrl": "https://github.com/ncsa/standalone-smm-analytics" + "repUrl": "https://github.com/ncsa/standalone-smm-analytics.git" + }, + { + "repType": "git", + "repUrl": "https://github.com/clowder-framework/smm-extractor.git" } ], "process": { diff --git a/network_analysis_extractor/requirement.txt b/network_analysis_extractor/requirement.txt new file mode 100644 index 0000000..d5f92cd --- /dev/null +++ b/network_analysis_extractor/requirement.txt @@ -0,0 +1 @@ +pyclowder==3.0.4 diff --git a/preprocessing/algorithm.py b/preprocessing/algorithm.py deleted file mode 100644 index 1dd28f6..0000000 --- a/preprocessing/algorithm.py +++ /dev/null @@ -1,37 +0,0 @@ -import plot -from preprocessing import Preprocess - - -def algorithm(df, params): - """ - wrapper function to put each individual algorithm inside - :param df: dataframe that contains all the input dataset - :param params: algorithm specific parameters - :return: a dictionary of { outputname: output content in memory } - """ - - output = {} - - # algorithm specific code - # construct sentiment analysis - PP = Preprocess(df, params['column']) - - output['phrases'] = PP.get_phrases() - output['filtered'] = filtered_tokens = PP.get_words() - output['processed'] = processed_tokens = PP.stem_lematize( - params['process'], filtered_tokens) - output['tagged'] = PP.tagging(params['tagger'], processed_tokens) - filtered_most_common, processed_most_common = PP.most_frequent( - filtered_tokens, processed_tokens) - output['most_common'] = processed_most_common - - # plot - index = [] - counts = [] - for common in processed_most_common[1:51]: - index.append(common[0]) - counts.append(common[1]) - title = 'Top 50 frequent words (' + params['process'] + ')' - output['div'] = plot.plot_bar_chart(index, counts, title) - - return output diff --git a/preprocessing/batch_function.py b/preprocessing/batch_function.py deleted file mode 100644 index 63ba1ea..0000000 --- a/preprocessing/batch_function.py +++ /dev/null @@ -1,58 +0,0 @@ -import dataset -import argparse -from notification import notification -from algorithm import algorithm - -if __name__ == '__main__': - - # entrance to invoke Batch - urls = {} - - # default parameters - parser = argparse.ArgumentParser(description="processing...") - parser.add_argument('--remoteReadPath', required=True) - parser.add_argument('--column', required=True) - parser.add_argument('--s3FolderName', required=True) - parser.add_argument('--uid', required=True) - parser.add_argument('--resultPath', required=True) - parser.add_argument('--email', required=True) - parser.add_argument('--sessionURL', required=True) - - # user specified parameters - parsed, unknown = parser.parse_known_args() - for arg in unknown: - if arg.startswith("--"): - parser.add_argument(arg, required=False) - - params = vars(parser.parse_args()) - - # arranging the paths - path = dataset.organize_path_lambda(params) - - # save the config file - urls['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - urls[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - urls[key] = value - - # push notification email - notification(toaddr=params['email'], case=3, filename=path['remoteSavePath'], - links=urls, sessionURL=params['sessionURL']) diff --git a/preprocessing/clear_cache.sh b/preprocessing/clear_cache.sh deleted file mode 100644 index 640c2ac..0000000 --- a/preprocessing/clear_cache.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env bash -CACHEDIR=/tmp - -# delete file older than 4 hours -find "$CACHEDIR" -maxdepth 1 -mindepth 1 -type d -mmin +240 -print0 | - while IFS= read -d '' -r dir; do - rm -rf "$dir" - done \ No newline at end of file diff --git a/preprocessing/clear_cache_cron b/preprocessing/clear_cache_cron deleted file mode 100644 index 0510c51..0000000 --- a/preprocessing/clear_cache_cron +++ /dev/null @@ -1 +0,0 @@ -0 * * * * /bin/bash /scripts/clear_cache.sh diff --git a/preprocessing/command.txt b/preprocessing/command.txt deleted file mode 100644 index 7220eb1..0000000 --- a/preprocessing/command.txt +++ /dev/null @@ -1,4 +0,0 @@ -# command to build and run this container -docker build -t socialmediamacroscope/preprocessing:latest . -docker build -f extractor.dockerfile -t socialmediamacroscope/preprocessing_extractor:latest . -docker push socialmediamacroscope/preprocessing_extractor:latest diff --git a/preprocessing/dataset.py b/preprocessing/dataset.py deleted file mode 100644 index c1bb7b5..0000000 --- a/preprocessing/dataset.py +++ /dev/null @@ -1,148 +0,0 @@ -import csv -import os -import json -import pickle -import pandas as pd -import types -import writeToS3 as s3 - - -def organize_path_lambda(event): - """ - parse the lambda handler parameter event to construct necessary paths for reading and storing data - :param event: aws lambda parameters from handler - :return: path dictionary - """ - # arranging the paths - localReadPath = os.path.join('/tmp', event['s3FolderName'], event['uid']) - localSavePath = os.path.join('/tmp', - event['s3FolderName'] + event['resultPath'], - event['uid']) - remoteSavePath = os.path.join(event['s3FolderName'] + event['resultPath'], - event['uid']) - if 'remoteReadPath' not in event.keys() or event['remoteReadPath'] == 'undefined': - remoteReadPath = remoteSavePath - filename = event['labeledFilename'] - else: - remoteReadPath = event['remoteReadPath'] - filename = remoteReadPath.split('/')[-2] + '.csv' - - if not os.path.exists(localSavePath): - os.makedirs(localSavePath) - if not os.path.exists(localReadPath): - os.makedirs(localReadPath) - - path = { - 'remoteReadPath': remoteReadPath, - 'localReadPath': localReadPath, - 'localSavePath': localSavePath, - 'remoteSavePath': remoteSavePath, - 'filename': filename - } - - return path - - -def get_remote_input(remoteReadPath, filename, localReadPath): - """ - download input file from s3 bucket to a local location, and then load - it to a pandas dataframe - :param remoteReadPath: remote path in s3 to store the data - :param localReadPath: local location to store the data, usually in /tmp - :return: df: dataframe that contains the complete input file - """ - s3.downloadToDisk(filename, localReadPath, remoteReadPath) - - # quick fix for decoding error, sometimes the data is coded in ISO-8859-1 - # Array = 2D nested list holding column and row data - Array = [] - try: - with open(os.path.join(localReadPath, filename), 'r', - encoding='utf-8', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - except Exception: - with open(os.path.join(localReadPath, filename), 'r', - encoding='ISO-8859-1', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - - # load to pandas dataframe - df = pd.DataFrame(Array[1:], columns=Array[0]) - - return df - - -def save_remote_output(localSavePath, remoteSavePath, fname, output_data): - """ - save output in memory first to local file, then upload to remote S3 bucket - :param localSavePath: local saved file - :param remoteSavePath: remote save file path - :param fname: filename - :param output_data: the actual data - :return: url of the file saved in S3 bucket - """ - - # json - if isinstance(output_data, dict): - fname += '.json' - with open(os.path.join(localSavePath, fname), 'w') as f: - json.dump(output_data, f) - - # dataframe to csv - elif isinstance(output_data, pd.DataFrame): - fname += '.csv' - output_data.to_csv(fname, encoding='utf-8') - - # string to html - elif isinstance(output_data, str): - fname += '.html' - with open(os.path.join(localSavePath, fname), 'w') as f: - f.write(output_data) - - # list(list) to csv - elif isinstance(output_data, list) \ - and (isinstance(output_data[0], list) or isinstance(output_data[0], - tuple)): - fname += '.csv' - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - writer = csv.writer(f) - for row in output_data: - try: - writer.writerow(row) - except UnicodeEncodeError as e: - print(e) - - # special case - elif isinstance(output_data, types.GeneratorType): - if fname == 'gephi': - fname += '.gml' - elif fname == 'pajek': - fname += '.net' - else: - fname += '.unknown' - - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - for line in output_data: - f.write(line + '\n') - - # else pickle the object - else: - fname += '.pickle' - with open(os.path.join(localSavePath, fname), 'wb') as f: - pickle.dump(output_data, f) - - s3.upload(localSavePath, remoteSavePath, fname) - url = s3.generate_downloads(remoteSavePath, fname) - - return url diff --git a/preprocessing/dockerfile b/preprocessing/dockerfile deleted file mode 100644 index a9511fa..0000000 --- a/preprocessing/dockerfile +++ /dev/null @@ -1,16 +0,0 @@ -FROM python:3.7 - -RUN apt-get -qq -y update && apt-get -qq -y install cron - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -RUN pip install --no-cache-dir -r requirement.txt -RUN python3 -m nltk.downloader -d /usr/local/share/nltk_data punkt stopwords averaged_perceptron_tagger - -# cron job clean tmp folder -RUN chmod u+x ./clear_cache.sh -RUN chmod 0644 ./clear_cache_cron -RUN crontab ./clear_cache_cron \ No newline at end of file diff --git a/preprocessing/extractor.dockerfile b/preprocessing/extractor.dockerfile deleted file mode 100644 index f97e9ce..0000000 --- a/preprocessing/extractor.dockerfile +++ /dev/null @@ -1,18 +0,0 @@ -FROM python:3.8.5 - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -RUN pip install --no-cache-dir -r requirement.txt -RUN python3 -m nltk.downloader -d /usr/local/share/nltk_data punkt stopwords averaged_perceptron_tagger wordnet - -# wordnet cannot unzip fix -RUN unzip /usr/local/share/nltk_data/corpora/wordnet.zip -d /usr/local/share/nltk_data/corpora - -# Command to be run when container is run -# Can add heartbeat to change the refresh rate -CMD python3 SmmExtractor.py --heartbeat 40 - -ENV MAIN_SCRIPT="SmmExtractor.py" diff --git a/preprocessing/notification.py b/preprocessing/notification.py deleted file mode 100644 index 176e095..0000000 --- a/preprocessing/notification.py +++ /dev/null @@ -1,170 +0,0 @@ -import smtplib -from email.mime.multipart import MIMEMultipart -from email.mime.text import MIMEText -import os - - -def notification(toaddr,case,filename,links,sessionURL): - # toaddr -- email address to send to - # text content to send - # subject - host = os.environ.get('EMAIL_HOST') - port = os.environ.get('EMAIL_PORT') - fromaddr = os.environ.get('EMAIL_FROM_ADDRESS') - password = os.environ.get('EMAIL_PASSWORD') - - if host is not None and host != "" and \ - port is not None and port != "" and\ - fromaddr is not None and fromaddr != "" and \ - password is not None and password != "": - # map the fpath component to History panel names - # local/NLP/sentiment/xxxxxxxxxxxxxxxxxxxxxxxx/ => [local,nlp,sentiment,xxxx,space] - # [local, GraphQL, reddit-post, aww, space] - # 0 1 2 3 - if filename != '': - fpath = filename.split('/') - - if fpath[1] == 'GraphQL': - fpath[1] = 'Social Media Data' - elif fpath[1] == 'NLP': - fpath[1] = 'Nature Language Processing' - elif fpath[1] == 'ML': - fpath[1] = 'Machine Learning ML' - elif fpath[1] == 'NW': - fpath[1] = 'Network Visualization and Analysis' - - if fpath[2] == 'reddit-Post': - fpath[2] = 'Subreddit Posts Title' - elif fpath[2] == 'reddit-Historical-Post': - fpath[2] = 'Reddit Historical Post' - elif fpath[2] == 'reddit-Search': - fpath[2] = 'Reddit Search Posts Title' - elif fpath[2] == 'sentiment': - fpath[2] = 'Sentiment Analysis' - elif fpath[2] == 'preprocessing': - fpath[2] = 'NLP Preprocessing' - elif fpath[2] == 'networkx': - fpath[2] = 'Python NetworkX' - elif fpath[2] == 'classification': - fpath[2] = 'Text Classification' - - if case == 0 or case == 'comment-fail': - html = """ - - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection has been terminated.

      -

      We are using the id and permalink from your Reddit Submission dataset - to collect comments and replies. It is most likely you have provide an incomplete Reddit Submission dataset missing these two fields.

      -

      Please try to reproduce the Reddit Submission with id and permalink, or switch to another dataset.

      - Go to your session... -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - - """ - subject = 'Your Reddit Comment collection has failed...' - elif case == 1 or case == 'comment-terminate': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is exceeding 400 Megabyte, and is terminated due to lack of disk space.

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. The partial comments we manage to collect and save will be compressed for you in an .zip file named """ + fpath[3] + """-comments.zip (click)
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
          -
          -

          Best Regards,

          -

          Social Media Macroscope - SMILE

          -
      - - """ - subject = 'Your Reddit Comment collection has been terminated...' - elif case == 2 or case == 'comment-success': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is ready for you!

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. It will be compressed for you in an .zip file named """+ fpath[3] +"""-comments.zip
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] +"""
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your Reddit Comment collection is completed!' - elif case == 3 or case == 'analytics-success': - list_html = '' - for key in links.keys(): - list_html += '
    • ' + key + '
      • ' - - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your """ + fpath[2] + """ results are ready for you! (job ID: """ + fpath[3] + """)

      -
        -
      • You can view the visualization and download the results at HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """ tab
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click view
          • -
        -
        -
      • You can also click the link below to download part of the results: -
          """ + list_html + """
        -
        • -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your ' + fpath[2] + ' computation is completed!' - - msg = MIMEMultipart('alternative') - msg['Subject'] = subject - msg['From'] = fromaddr - msg['To'] = toaddr - msg.attach(MIMEText(html, 'html')) - - server = smtplib.SMTP_SSL(host, port) - server.login(fromaddr, password) - server.sendmail(fromaddr, toaddr, msg.as_string()) - server.quit() - else: - print("Invalid Email host setting! Skip notification.") diff --git a/preprocessing/plot.py b/preprocessing/plot.py deleted file mode 100644 index 431d2ff..0000000 --- a/preprocessing/plot.py +++ /dev/null @@ -1,157 +0,0 @@ -from plotly.graph_objs import * -from plotly.offline import plot -import networkx as nx - - -def plot_pie_chart(labels, values, title): - """ - plot pie chart - :param labels: list of label, shape must match parameter values - :param values: list of values, shape must match parameter labels - :param title: title to show - :return: html code in a div - """ - trace = Pie(labels=labels, values=values, textinfo='label+percent') - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_network(graph, layout, relationships, title): - """ - plot network graph - :param graph: networkx graph - :param layout: network layout - :param relationships: reply, retweet, mention or anything else - :param title: title to show - :return: html code in a div - """ - - if layout == 'spring': - pos = nx.spring_layout(graph) - elif layout == 'circular': - pos = nx.circular_layout(graph) - elif layout == 'fruchterman': - pos = nx.fruchterman_reingold_layout(graph) - elif layout == 'random': - pos = nx.random_layout(graph) - elif layout == 'shell': - pos = nx.shell_layout(graph) - elif layout == 'spectral': - pos = nx.spectral_layout(graph) - edge_attri = nx.get_edge_attributes(graph, 'text') - edge_trace = Scatter(x=[], y=[], text=[], - line=Line(width=1, color='#b5b5b5'), - hoverinfo='text', - mode='lines', - hoveron='points') - for edge in graph.edges(): - x0, y0 = pos[edge[0]] - x1, y1 = pos[edge[1]] - edge_trace['x'] += [x0, x1, None] - edge_trace['y'] += [y0, y1, None] - edge_trace['text'].append(edge_attri[edge]) - - node_trace = Scatter(x=[], y=[], text=[], mode='markers', - hoverinfo='text', hoveron='points+fills', - marker=Marker(showscale=True, - colorscale='Portland', - reversescale=False, - color=[], - size=10, - colorbar=dict(thickness=15, - title='node in-degree plus out-degree', - xanchor='left', - titleside='right'), - line=dict(width=2))) - for node in graph.nodes(): - x, y = pos[node] - node_trace['x'].append(x) - node_trace['y'].append(y) - - # set label - for node in graph.nodes(): - node_trace['marker']['color'].append(graph.in_degree()[node] + graph.out_degree()[node]) - if relationships == 'reply_to': - node_trace['text'].append("@" + node + " is replied by " - + str(graph.in_degree()[node]) - + " user(s), and replies to " - + str(graph.out_degree()[node]) + " user(s)") - - elif relationships == 'retweet_from': - node_trace['text'].append("@" + node + " is retweeted by " - + str(graph.in_degree()[node]) - + " user(s) and retweets from " - + str(graph.out_degree()[node]) - + " user(s)") - - elif relationships == 'mentions': - node_trace['text'].append("@" + node + " is mentioned by " - + str(graph.in_degree()[node]) - + " user(s) and mentions " - + str(graph.out_degree()[node]) - + " user(s)") - - fig = Figure(data=Data([edge_trace, node_trace]), - layout=Layout( - title=title, - titlefont=dict(size=16), showlegend=False, - hovermode='closest', margin=dict(b=20, l=5, r=5, t=40), - xaxis=XAxis(showgrid=False, zeroline=False, - showticklabels=False), - yaxis=YAxis(showgrid=False, zeroline=False, - showticklabels=False) - )) - - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_bar_chart(index, counts, title): - """ - plot bar chart - :param index: x - axis, usually the index - :param counts: y - axis, usually counts - :param title: - :return: - """ - - trace = Bar(x=index, y=counts, - marker=dict(color='rgba(200,75,73,1.0)', - line=dict(color='rgba(111,11,9,1.0)',width=2))) - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - yaxis=dict( - showgrid=True, - showline=True, - showticklabels=True, - linecolor='rgba(102, 102, 102, 0.8)', - linewidth=2 - ), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, image_filename='plot_img') - - return div diff --git a/preprocessing/postToAWSBatch.py b/preprocessing/postToAWSBatch.py deleted file mode 100644 index 73c93f5..0000000 --- a/preprocessing/postToAWSBatch.py +++ /dev/null @@ -1,24 +0,0 @@ -import boto3 -import os - -client = boto3.client('batch', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(jobDefinition, jobName, jobQueue, command): - command.extend(['--HOST_IP', os.environ['HOST_IP'], - '--AWS_ACCESSKEY', os.environ['AWS_ACCESSKEY'], - '--AWS_ACCESSKEYSECRET', os.environ['AWS_ACCESSKEYSECRET'], - '--BUCKET_NAME', os.environ['BUCKET_NAME']]) - - response = client.submit_job( - jobDefinition=jobDefinition, - jobName=jobName, - jobQueue=jobQueue, - containerOverrides={ - 'vcpus': 2, - 'memory': 2048, - 'command': command - } - ) - - return response \ No newline at end of file diff --git a/preprocessing/postToAWSLambda.py b/preprocessing/postToAWSLambda.py deleted file mode 100644 index 8a227e7..0000000 --- a/preprocessing/postToAWSLambda.py +++ /dev/null @@ -1,28 +0,0 @@ -import json -import boto3 -import os - -client = boto3.client('lambda', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(function_name, args): - - # pass information so remote lambda can access local s3 minio - args['HOST_IP'] = os.environ['HOST_IP'] - args['AWS_ACCESSKEY'] = os.environ['AWS_ACCESSKEY'] - args['AWS_ACCESSKEYSECRET'] = os.environ['AWS_ACCESSKEYSECRET'] - args['BUCKET_NAME'] = os.environ['BUCKET_NAME'] - - response = client.invoke( - Payload=json.dumps(args), - FunctionName=function_name, - InvocationType='RequestResponse', - LogType='Tail', - ) - - if response['StatusCode'] == 200: - results = json.loads(response['Payload'].read().decode('utf-8')) - else: - results = {'ERROR': response['FunctionError']} - - return results \ No newline at end of file diff --git a/preprocessing/preprocessing.py b/preprocessing/preprocessing.py deleted file mode 100755 index 6e520cd..0000000 --- a/preprocessing/preprocessing.py +++ /dev/null @@ -1,147 +0,0 @@ -import itertools -from nltk.tokenize import TweetTokenizer -from nltk.corpus import stopwords -from nltk import WordNetLemmatizer, FreqDist, pos_tag, PorterStemmer -import re -import os - - -class Preprocess: - - def __init__(self, df, column): - - self.id_column = "id" - if 'id_str' in df.columns: - self.id_column = 'id_str' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif 'id' in df.columns: - self.id_column = 'id' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif 'comment_id' in df.columns: - self.id_column = 'comment_id' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif '_source.id_str': - self.id_column = '_source.id_str' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif '_source.id': - self.id_column = '_source.id_str' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - else: - sentences = df[df[column] != ''][column].dropna().astype( - 'str').tolist() - self.id = [] - - sentences = [re.sub(r"http\S+", "", tweet) for tweet in sentences] - self.sentences = sentences - - def get_phrases(self): - phrases = [[self.id_column, 'Phrase']] - puncList = '.;,:!?/\\)(\"\'' - regex = re.compile('[%s%s]' % (puncList, '|\t\n')) - for sent_id, sent in itertools.zip_longest(self.id, self.sentences): - for phrase in regex.split(sent): - if phrase != '' and phrase.isdigit() == False and len( - phrase) > 20: - phrases.append([sent_id, phrase.lower()]) - - return phrases - - def get_words(self): - tknz = TweetTokenizer() - tokens = [tknz.tokenize(t) for t in self.sentences] - - # nltk's stopwords are too weak - with open(os.path.dirname(__file__) + '/stopwords_en.txt', 'r') as f: - stopwords2 = f.read().split('\n') - with open(os.path.dirname(__file__) + '/twitter-customized.txt', - 'r') as f: - stopwords3 = f.read().split(',') - - filtered_tokens = [[self.id_column, 'Filtered Word']] - for sent_id, token in itertools.zip_longest(self.id, tokens): - # third party stopwors:https://sites.google.com/site/kevinbouge/stopwords-lists - # twitter specific stopwordshttps://sites.google.com/site/iamgongwei/home/sw - # nltk stopwords - temp = [sent_id] - for word in token: - if word.lower() not in stopwords.words('english') \ - and word.lower() not in stopwords2 \ - and (word.isalpha() == True or word[0] == '#' or word[0] == '$') \ - and word.lower() not in stopwords3: - temp.append(word) - filtered_tokens.append(temp) - - return filtered_tokens - - def stem_lematize(self, process, filtered_tokens, header=True): - if header: - filtered_tokens = filtered_tokens[1:] - processed_tokens = [] - if process == 'lemmatization': - processed_tokens.append([self.id_column, 'lemmatization']) - wnl = WordNetLemmatizer() - for tk in filtered_tokens: - processed_tokens.append([wnl.lemmatize(t) for t in tk]) - elif process == 'stemming': - processed_tokens.append([self.id_column, 'stemming']) - porter = PorterStemmer() - for tk in filtered_tokens: - processed_tokens.append([porter.stem(t) for t in tk]) - elif process == 'both': - processed_tokens.append([self.id_column, 'lemmatization+stemming']) - wnl = WordNetLemmatizer() - porter = PorterStemmer() - for tk in filtered_tokens: - processed_tokens.append( - [wnl.lemmatize(porter.stem(t)) for t in tk]) - - return processed_tokens - - def tagging(self, tagger, processed_tokens): - tag = [[self.id_column, "POS tags"]] - for sent_id, sent_token in itertools.zip_longest(self.id, processed_tokens): - if tagger == 'posTag': - # don't add id if exist: - if len(self.id) > 0: - tag.append([sent_id] + pos_tag(sent_token[1:])) - else: - tag.append([None] + pos_tag(sent_token)) - - return tag - - def most_frequent(self, filtered_tokens, processed_tokens, header=True): - if header: - filtered_tokens = filtered_tokens[1:] - processed_tokens = processed_tokens[1:] - - filtered_document = [] - for sent_token in filtered_tokens: - # don't add id to the total document - if len(self.id) > 0: - filtered_document += sent_token[1:] - else: - filtered_document += sent_token - - processed_document = [] - for sent_token in processed_tokens: - if len(self.id) > 0: - processed_document += sent_token[1:] - else: - filtered_document += sent_token - - filtered_most_common = FreqDist(filtered_document).most_common() - filtered_most_common.insert(0, ['word', 'frequency']) - processed_most_common = FreqDist(processed_document).most_common() - processed_most_common.insert(0, ['word', 'frequency']) - - return filtered_most_common, processed_most_common diff --git a/preprocessing/rabbitmq_handler.py b/preprocessing/rabbitmq_handler.py deleted file mode 100644 index 247844c..0000000 --- a/preprocessing/rabbitmq_handler.py +++ /dev/null @@ -1,99 +0,0 @@ -import json -import os -import traceback - -import dataset -import pika -from algorithm import algorithm - -import postToAWSLambda -import postToAWSBatch - - -def rabbitmq_handler(ch, method, properties, body): - try: - msg = {} - - # determine if it goes to aws, lambda, or batch - params = json.loads(body) - - if params['platform'] == 'aws-lambda': - msg = postToAWSLambda.invoke(params['function_name'], params) - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - elif params['platform'] == 'lambda': - path = dataset.organize_path_lambda(params) - - # save the config file - msg['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - msg[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - msg[key] = value - - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - elif params['platform'] == 'aws-batch': - postToAWSBatch.invoke(params['jobDefinition'], - params['jobName'], - params['jobQueue'], - params['command']) - - elif params['platform'] == 'batch': - os.system(' '.join(params['command'])) - - else: - raise ValueError( - 'Rabbitmq Message Not Recognizable. ' - 'It has to specify what platform to run: aws-lambda, aws-batch, lambda or batch.') - - except BaseException as e: - - msg = {'ERROR': - {'message': str(e), - 'traceback': traceback.format_exc() - } - } - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - return None - - -if __name__ == '__main__': - connection = pika.BlockingConnection(pika.ConnectionParameters(port=5672, host="rabbitmq")) - channel = connection.channel() - - # pass the queue name in environment variable - queue = os.environ['QUEUE_NAME'] - - channel.queue_declare(queue=queue) - channel.basic_qos(prefetch_count=1) - channel.basic_consume(queue=queue, on_message_callback=rabbitmq_handler, auto_ack=True) - channel.start_consuming() diff --git a/preprocessing/requirement.txt b/preprocessing/requirement.txt deleted file mode 100644 index 69c130c..0000000 --- a/preprocessing/requirement.txt +++ /dev/null @@ -1,8 +0,0 @@ -boto3>=1.6.11 -numpy>=1.16.1 -pandas>=0.24.1 -plotly==2.7.0 -nltk>=3.2.5 -pika>=1.1.0 -networkx==1.11 -pyclowder==2.7.0 diff --git a/preprocessing/stopwords_en.txt b/preprocessing/stopwords_en.txt deleted file mode 100644 index 76525dc..0000000 --- a/preprocessing/stopwords_en.txt +++ /dev/null @@ -1,571 +0,0 @@ -a -a's -able -about -above -according -accordingly -across -actually -after -afterwards -again -against -ain't -all -allow -allows -almost -alone -along -already -also -although -always -am -among -amongst -an -and -another -any -anybody -anyhow -anyone -anything -anyway -anyways -anywhere -apart -appear -appreciate -appropriate -are -aren't -around -as -aside -ask -asking -associated -at -available -away -awfully -b -be -became -because -become -becomes -becoming -been -before -beforehand -behind -being -believe -below -beside -besides -best -better -between -beyond -both -brief -but -by -c -c'mon -c's -came -can -can't -cannot -cant -cause -causes -certain -certainly -changes -clearly -co -com -come -comes -concerning -consequently -consider -considering -contain -containing -contains -corresponding -could -couldn't -course -currently -d -definitely -described -despite -did -didn't -different -do -does -doesn't -doing -don't -done -down -downwards -during -e -each -edu -eg -eight -either -else -elsewhere -enough -entirely -especially -et -etc -even -ever -every -everybody -everyone -everything -everywhere -ex -exactly -example -except -f -far -few -fifth -first -five -followed -following -follows -for -former -formerly -forth -four -from -further -furthermore -g -get -gets -getting -given -gives -go -goes -going -gone -got -gotten -greetings -h -had -hadn't -happens -hardly -has -hasn't -have -haven't -having -he -he's -hello -help -hence -her -here -here's -hereafter -hereby -herein -hereupon -hers -herself -hi -him -himself -his -hither -hopefully -how -howbeit -however -i -i'd -i'll -i'm -i've -ie -if -ignored -immediate -in -inasmuch -inc -indeed -indicate -indicated -indicates -inner -insofar -instead -into -inward -is -isn't -it -it'd -it'll -it's -its -itself -j -just -k -keep -keeps -kept -know -knows -known -l -last -lately -later -latter -latterly -least -less -lest -let -let's -like -liked -likely -little -look -looking -looks -ltd -m -mainly -many -may -maybe -me -mean -meanwhile -merely -might -more -moreover -most -mostly -much -must -my -myself -n -name -namely -nd -near -nearly -necessary -need -needs -neither -never -nevertheless -new -next -nine -no -nobody -non -none -noone -nor -normally -not -nothing -novel -now -nowhere -o -obviously -of -off -often -oh -ok -okay -old -on -once -one -ones -only -onto -or -other -others -otherwise -ought -our -ours -ourselves -out -outside -over -overall -own -p -particular -particularly -per -perhaps -placed -please -plus -possible -presumably -probably -provides -q -que -quite -qv -r -rather -rd -re -really -reasonably -regarding -regardless -regards -relatively -respectively -right -s -said -same -saw -say -saying -says -second -secondly -see -seeing -seem -seemed -seeming -seems -seen -self -selves -sensible -sent -serious -seriously -seven -several -shall -she -should -shouldn't -since -six -so -some -somebody -somehow -someone -something -sometime -sometimes -somewhat -somewhere -soon -sorry -specified -specify -specifying -still -sub -such -sup -sure -t -t's -take -taken -tell -tends -th -than -thank -thanks -thanx -that -that's -thats -the -their -theirs -them -themselves -then -thence -there -there's -thereafter -thereby -therefore -therein -theres -thereupon -these -they -they'd -they'll -they're -they've -think -third -this -thorough -thoroughly -those -though -three -through -throughout -thru -thus -to -together -too -took -toward -towards -tried -tries -truly -try -trying -twice -two -u -un -under -unfortunately -unless -unlikely -until -unto -up -upon -us -use -used -useful -uses -using -usually -uucp -v -value -various -very -via -viz -vs -w -want -wants -was -wasn't -way -we -we'd -we'll -we're -we've -welcome -well -went -were -weren't -what -what's -whatever -when -whence -whenever -where -where's -whereafter -whereas -whereby -wherein -whereupon -wherever -whether -which -while -whither -who -who's -whoever -whole -whom -whose -why -will -willing -wish -with -within -without -won't -wonder -would -would -wouldn't -x -y -yes -yet -you -you'd -you'll -you're -you've -your -yours -yourself -yourselves -z -zero diff --git a/preprocessing/twitter-customized.txt b/preprocessing/twitter-customized.txt deleted file mode 100644 index 3c30d1d..0000000 --- a/preprocessing/twitter-customized.txt +++ /dev/null @@ -1 +0,0 @@ -.com,.ly,.net,.org,aahh,aarrgghh,babeh,blah,bleh,hmm,http,https,huh,huhu,huhuhu,laa,laaa,lah,leh,rss,rt, diff --git a/preprocessing/writeToS3.py b/preprocessing/writeToS3.py deleted file mode 100644 index 4fba54f..0000000 --- a/preprocessing/writeToS3.py +++ /dev/null @@ -1,77 +0,0 @@ -import boto3 -import mimetypes -import os -from botocore.client import Config - -client = boto3.client('s3', endpoint_url='http://' + os.environ['HOST_IP'] + ':9000', - aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET'], - config=Config(signature_version='s3v4')) - -bucket_name = os.environ['BUCKET_NAME'] - -def upload(localpath, remotepath, filename): - content_type = mimetypes.guess_type(os.path.join(localpath,filename))[0] - print(filename, content_type) - if content_type == None: - extra_args = {'ContentType':'application/octet-stream'} - else: - extra_args = {'ContentType':content_type} - - client.upload_file(os.path.join(localpath, filename), - bucket_name, - os.path.join(remotepath, filename), - ExtraArgs=extra_args) - - -def createDirectory(DirectoryName): - client.put_object(Bucket=bucket_name, Key=DirectoryName) - - -def generate_downloads(remotepath, filename): - url = client.generate_presigned_url( - ClientMethod='get_object', - Params={ - 'Bucket': bucket_name, - 'Key': os.path.join(remotepath, filename) - }, - ExpiresIn=604800 # one week - ) - - return url - - -def downloadToDisk(filename, localpath, remotepath): - with open(os.path.join(localpath, filename), 'wb') as f: - client.download_fileobj(bucket_name, - os.path.join(remotepath, filename), f) - - -def getObject(remoteKey): - obj = client.get_object(Bucket=bucket_name, Key=remoteKey) - - -def putObject(body, remoteKey): - # bytes or seekable file-like object - obj = client.put_object(Bucket=bucket_name, - Body=body, Key=remoteKey) - print(obj['Body'].read()) - -def listDir(remoteClass): - objects = client.list_objects(Bucket=bucket_name, - Prefix=remoteClass, - Delimiter='/') - foldernames = [] - for o in objects.get('CommonPrefixes'): - foldernames.append(o.get('Prefix')) - - # only return the list of foldernames - return foldernames - - -def listFiles(foldernames): - objects = client.list_objects(Bucket=bucket_name, - Prefix=foldernames) - - # return rich information about the files - return objects.get('Contents') diff --git a/preprocessing_extractor/CHANGELOG.md b/preprocessing_extractor/CHANGELOG.md new file mode 100644 index 0000000..bb99d11 --- /dev/null +++ b/preprocessing_extractor/CHANGELOG.md @@ -0,0 +1,16 @@ +# Changelog +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.1.0] - 03-15-2023 + +### Added +- Initial release of the preprocessing extractor + + +## [0.1.1] - 10-03-2023 + +### Changed +- Support Clowder V2 [#1](https://github.com/clowder-framework/smm-extractor/issues/1) diff --git a/preprocessing_extractor/README.md b/preprocessing_extractor/README.md new file mode 100644 index 0000000..e69de29 diff --git a/sentiment_analysis/SmmExtractor.py b/preprocessing_extractor/SmmExtractor.py similarity index 95% rename from sentiment_analysis/SmmExtractor.py rename to preprocessing_extractor/SmmExtractor.py index 6aefd2c..d79adf2 100644 --- a/sentiment_analysis/SmmExtractor.py +++ b/preprocessing_extractor/SmmExtractor.py @@ -103,6 +103,7 @@ def process_message(self, connector, host, secret_key, resource, parameters): # execute the algorithm # Parse user parameters to determine which column to analyze userParams = parameters.get('parameters') + output = algorithm(df, userParams) connector.message_process(resource, "Running the algorithm...") @@ -110,14 +111,15 @@ def process_message(self, connector, host, secret_key, resource, parameters): for fname, output_data in output.items(): if fname != 'uid': local_output_path = save_local_output("", fname, output_data) - uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, - local_output_path) connector.message_process(resource, "Saving " + local_output_path + "...") + uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, + local_output_path) + connector.message_process(resource, local_output_path + " saved...") - # write params to metadata + connector.message_process(resource, "Writing metadata...") metadata = self.get_metadata(userParams, 'file', uploaded_file_id, host) pyclowder.files.upload_metadata(connector, host, secret_key, uploaded_file_id, metadata) - connector.message_process(resource, "Writing metadata...") + connector.message_process(resource, "Metadata written...") if __name__ == "__main__": diff --git a/preprocessing_extractor/extractor.dockerfile b/preprocessing_extractor/extractor.dockerfile new file mode 100644 index 0000000..72b1ac2 --- /dev/null +++ b/preprocessing_extractor/extractor.dockerfile @@ -0,0 +1,18 @@ +FROM socialmediamacroscope/preprocessing:latest + +RUN mkdir -p /scripts +WORKDIR /scripts + +COPY ./SmmExtractor.py ./ +COPY ./extractor_info.json ./ +COPY ./requirement.txt ./extractor-requirement.txt + +# Install pyClowder and any other python dependencies +RUN pip3 install --no-cache-dir -r ./extractor-requirement.txt -U + +# Command to be run when container is run +# Can add heartbeat to change the refresh rate +CMD python3 SmmExtractor.py --heartbeat 300 + +ENV MAIN_SCRIPT="SmmExtractor.py" \ + CLOWDER_VERSION=1 diff --git a/preprocessing/extractor_info.json b/preprocessing_extractor/extractor_info.json similarity index 70% rename from preprocessing/extractor_info.json rename to preprocessing_extractor/extractor_info.json index ec9affe..fd3e33b 100644 --- a/preprocessing/extractor_info.json +++ b/preprocessing_extractor/extractor_info.json @@ -1,15 +1,19 @@ { "@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", "name": "smm.preprocessing.analysis", - "version": "0.1", - "description": "Description on this extractor", + "version": "0.1.1", + "description": "Tokenization is the process of dividing written text into meaningful units, such as words, sentences , or topics. Lemmatization and Stemming reduces word forms to common base words. Part-of-speech Tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.", "author": "Wang, Chen ", "contributors": [], "contexts": [{}], "repository": [ { "repType": "git", - "repUrl": "https://github.com/ncsa/standalone-smm-analytics" + "repUrl": "https://github.com/ncsa/standalone-smm-analytics.git" + }, + { + "repType": "git", + "repUrl": "https://github.com/clowder-framework/smm-extractor.git" } ], "process": { diff --git a/preprocessing_extractor/requirement.txt b/preprocessing_extractor/requirement.txt new file mode 100644 index 0000000..d5f92cd --- /dev/null +++ b/preprocessing_extractor/requirement.txt @@ -0,0 +1 @@ +pyclowder==3.0.4 diff --git a/requirement.txt b/requirement.txt new file mode 100644 index 0000000..d5f92cd --- /dev/null +++ b/requirement.txt @@ -0,0 +1 @@ +pyclowder==3.0.4 diff --git a/sentiment_analysis/README.md b/sentiment_analysis/README.md deleted file mode 100644 index ecdc9e9..0000000 --- a/sentiment_analysis/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Prerequisit - -Before building the image, please download the pre-trailed model at [LINK](https://uofi.box.com/s/g6dnk5e8exwukmhpzhvhpard13956sb6) and place it under the root -directorate of the **sentiment_analysis** folder - -This model is essential to run the sentiment analysis debiasd model. Details please refer to here: Speer, R. (2017). How to make a racist AI without really trying. http://blog.conceptnet.io/posts/2017/how-to-make-a-racist-ai-without-really-trying - diff --git a/sentiment_analysis/algorithm.py b/sentiment_analysis/algorithm.py deleted file mode 100644 index 2677334..0000000 --- a/sentiment_analysis/algorithm.py +++ /dev/null @@ -1,35 +0,0 @@ -import plot -from sentiment_analysis import Sentiment - - -def algorithm(df, params): - """ - wrapper function to put each individual algorithm inside - :param df: dataframe that contains all the input dataset - :param params: algorithm specific parameters - :return: a dictionary of { outputname: output content in memory } - """ - - output = {} - - # algorithm specific code - # construct sentiment analysis - SA = Sentiment(df, params['column']) - - sentiment_sentence, sentiment_doc = SA.sentiment(params['algorithm']) - output['sentiment'] = sentiment_sentence - output['doc'] = sentiment_doc - - if params['algorithm'] == 'vader': - output['negation'] = SA.negated() - output['allcap'] = SA.allcap() - - # plot - if 'neg' in sentiment_doc.keys() and 'neu' in sentiment_doc.keys() and 'pos' in sentiment_doc.keys(): - labels = ['negative', 'neutral', 'positive'] - values = [sentiment_doc['neg'], sentiment_doc['neu'], - sentiment_doc['pos']] - output['div'] = plot.plot_pie_chart(labels, values, - title='Sentiment of the dataset') - - return output diff --git a/sentiment_analysis/batch_function.py b/sentiment_analysis/batch_function.py deleted file mode 100644 index 63ba1ea..0000000 --- a/sentiment_analysis/batch_function.py +++ /dev/null @@ -1,58 +0,0 @@ -import dataset -import argparse -from notification import notification -from algorithm import algorithm - -if __name__ == '__main__': - - # entrance to invoke Batch - urls = {} - - # default parameters - parser = argparse.ArgumentParser(description="processing...") - parser.add_argument('--remoteReadPath', required=True) - parser.add_argument('--column', required=True) - parser.add_argument('--s3FolderName', required=True) - parser.add_argument('--uid', required=True) - parser.add_argument('--resultPath', required=True) - parser.add_argument('--email', required=True) - parser.add_argument('--sessionURL', required=True) - - # user specified parameters - parsed, unknown = parser.parse_known_args() - for arg in unknown: - if arg.startswith("--"): - parser.add_argument(arg, required=False) - - params = vars(parser.parse_args()) - - # arranging the paths - path = dataset.organize_path_lambda(params) - - # save the config file - urls['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - urls[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - urls[key] = value - - # push notification email - notification(toaddr=params['email'], case=3, filename=path['remoteSavePath'], - links=urls, sessionURL=params['sessionURL']) diff --git a/sentiment_analysis/clear_cache.sh b/sentiment_analysis/clear_cache.sh deleted file mode 100644 index 640c2ac..0000000 --- a/sentiment_analysis/clear_cache.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env bash -CACHEDIR=/tmp - -# delete file older than 4 hours -find "$CACHEDIR" -maxdepth 1 -mindepth 1 -type d -mmin +240 -print0 | - while IFS= read -d '' -r dir; do - rm -rf "$dir" - done \ No newline at end of file diff --git a/sentiment_analysis/clear_cache_cron b/sentiment_analysis/clear_cache_cron deleted file mode 100644 index 0510c51..0000000 --- a/sentiment_analysis/clear_cache_cron +++ /dev/null @@ -1 +0,0 @@ -0 * * * * /bin/bash /scripts/clear_cache.sh diff --git a/sentiment_analysis/command.txt b/sentiment_analysis/command.txt deleted file mode 100644 index 20b8bc0..0000000 --- a/sentiment_analysis/command.txt +++ /dev/null @@ -1,3 +0,0 @@ -# command to build and run this container -docker build -t socialmediamacroscope/sentiment_analysis:latest . -docker build -f extractor.dockerfile -t socialmediamacroscope/sentiment_analysis_extractor:latest . diff --git a/sentiment_analysis/dataset.py b/sentiment_analysis/dataset.py deleted file mode 100644 index c1bb7b5..0000000 --- a/sentiment_analysis/dataset.py +++ /dev/null @@ -1,148 +0,0 @@ -import csv -import os -import json -import pickle -import pandas as pd -import types -import writeToS3 as s3 - - -def organize_path_lambda(event): - """ - parse the lambda handler parameter event to construct necessary paths for reading and storing data - :param event: aws lambda parameters from handler - :return: path dictionary - """ - # arranging the paths - localReadPath = os.path.join('/tmp', event['s3FolderName'], event['uid']) - localSavePath = os.path.join('/tmp', - event['s3FolderName'] + event['resultPath'], - event['uid']) - remoteSavePath = os.path.join(event['s3FolderName'] + event['resultPath'], - event['uid']) - if 'remoteReadPath' not in event.keys() or event['remoteReadPath'] == 'undefined': - remoteReadPath = remoteSavePath - filename = event['labeledFilename'] - else: - remoteReadPath = event['remoteReadPath'] - filename = remoteReadPath.split('/')[-2] + '.csv' - - if not os.path.exists(localSavePath): - os.makedirs(localSavePath) - if not os.path.exists(localReadPath): - os.makedirs(localReadPath) - - path = { - 'remoteReadPath': remoteReadPath, - 'localReadPath': localReadPath, - 'localSavePath': localSavePath, - 'remoteSavePath': remoteSavePath, - 'filename': filename - } - - return path - - -def get_remote_input(remoteReadPath, filename, localReadPath): - """ - download input file from s3 bucket to a local location, and then load - it to a pandas dataframe - :param remoteReadPath: remote path in s3 to store the data - :param localReadPath: local location to store the data, usually in /tmp - :return: df: dataframe that contains the complete input file - """ - s3.downloadToDisk(filename, localReadPath, remoteReadPath) - - # quick fix for decoding error, sometimes the data is coded in ISO-8859-1 - # Array = 2D nested list holding column and row data - Array = [] - try: - with open(os.path.join(localReadPath, filename), 'r', - encoding='utf-8', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - except Exception: - with open(os.path.join(localReadPath, filename), 'r', - encoding='ISO-8859-1', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - - # load to pandas dataframe - df = pd.DataFrame(Array[1:], columns=Array[0]) - - return df - - -def save_remote_output(localSavePath, remoteSavePath, fname, output_data): - """ - save output in memory first to local file, then upload to remote S3 bucket - :param localSavePath: local saved file - :param remoteSavePath: remote save file path - :param fname: filename - :param output_data: the actual data - :return: url of the file saved in S3 bucket - """ - - # json - if isinstance(output_data, dict): - fname += '.json' - with open(os.path.join(localSavePath, fname), 'w') as f: - json.dump(output_data, f) - - # dataframe to csv - elif isinstance(output_data, pd.DataFrame): - fname += '.csv' - output_data.to_csv(fname, encoding='utf-8') - - # string to html - elif isinstance(output_data, str): - fname += '.html' - with open(os.path.join(localSavePath, fname), 'w') as f: - f.write(output_data) - - # list(list) to csv - elif isinstance(output_data, list) \ - and (isinstance(output_data[0], list) or isinstance(output_data[0], - tuple)): - fname += '.csv' - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - writer = csv.writer(f) - for row in output_data: - try: - writer.writerow(row) - except UnicodeEncodeError as e: - print(e) - - # special case - elif isinstance(output_data, types.GeneratorType): - if fname == 'gephi': - fname += '.gml' - elif fname == 'pajek': - fname += '.net' - else: - fname += '.unknown' - - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - for line in output_data: - f.write(line + '\n') - - # else pickle the object - else: - fname += '.pickle' - with open(os.path.join(localSavePath, fname), 'wb') as f: - pickle.dump(output_data, f) - - s3.upload(localSavePath, remoteSavePath, fname) - url = s3.generate_downloads(remoteSavePath, fname) - - return url diff --git a/sentiment_analysis/dockerfile b/sentiment_analysis/dockerfile deleted file mode 100644 index f5773c1..0000000 --- a/sentiment_analysis/dockerfile +++ /dev/null @@ -1,17 +0,0 @@ -FROM python:3.8.5 - -RUN apt-get -qq -y update && apt-get -qq -y install cron - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -RUN pip install --no-cache-dir -r requirement.txt -RUN python3 -m nltk.downloader -d /usr/local/share/nltk_data stopwords wordnet punkt averaged_perceptron_tagger \ -vader_lexicon sentiwordnet - -# cron job clean tmp folder -RUN chmod u+x ./clear_cache.sh -RUN chmod 0644 ./clear_cache_cron -RUN crontab ./clear_cache_cron \ No newline at end of file diff --git a/sentiment_analysis/extractor.dockerfile b/sentiment_analysis/extractor.dockerfile deleted file mode 100644 index f46bfdb..0000000 --- a/sentiment_analysis/extractor.dockerfile +++ /dev/null @@ -1,20 +0,0 @@ -FROM python:3.8.5 - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -# Install pyClowder and any other python dependencies -RUN pip install --no-cache-dir -r requirement.txt -RUN python3 -m nltk.downloader -d /usr/local/share/nltk_data stopwords wordnet punkt averaged_perceptron_tagger \ -vader_lexicon sentiwordnet - -# wordnet cannot unzip fix -RUN unzip /usr/local/share/nltk_data/corpora/wordnet.zip -d /usr/local/share/nltk_data/corpora - -# Command to be run when container is run -# Can add heartbeat to change the refresh rate -CMD python3 SmmExtractor.py --heartbeat 40 - -ENV MAIN_SCRIPT="SmmExtractor.py" diff --git a/sentiment_analysis/notification.py b/sentiment_analysis/notification.py deleted file mode 100644 index 176e095..0000000 --- a/sentiment_analysis/notification.py +++ /dev/null @@ -1,170 +0,0 @@ -import smtplib -from email.mime.multipart import MIMEMultipart -from email.mime.text import MIMEText -import os - - -def notification(toaddr,case,filename,links,sessionURL): - # toaddr -- email address to send to - # text content to send - # subject - host = os.environ.get('EMAIL_HOST') - port = os.environ.get('EMAIL_PORT') - fromaddr = os.environ.get('EMAIL_FROM_ADDRESS') - password = os.environ.get('EMAIL_PASSWORD') - - if host is not None and host != "" and \ - port is not None and port != "" and\ - fromaddr is not None and fromaddr != "" and \ - password is not None and password != "": - # map the fpath component to History panel names - # local/NLP/sentiment/xxxxxxxxxxxxxxxxxxxxxxxx/ => [local,nlp,sentiment,xxxx,space] - # [local, GraphQL, reddit-post, aww, space] - # 0 1 2 3 - if filename != '': - fpath = filename.split('/') - - if fpath[1] == 'GraphQL': - fpath[1] = 'Social Media Data' - elif fpath[1] == 'NLP': - fpath[1] = 'Nature Language Processing' - elif fpath[1] == 'ML': - fpath[1] = 'Machine Learning ML' - elif fpath[1] == 'NW': - fpath[1] = 'Network Visualization and Analysis' - - if fpath[2] == 'reddit-Post': - fpath[2] = 'Subreddit Posts Title' - elif fpath[2] == 'reddit-Historical-Post': - fpath[2] = 'Reddit Historical Post' - elif fpath[2] == 'reddit-Search': - fpath[2] = 'Reddit Search Posts Title' - elif fpath[2] == 'sentiment': - fpath[2] = 'Sentiment Analysis' - elif fpath[2] == 'preprocessing': - fpath[2] = 'NLP Preprocessing' - elif fpath[2] == 'networkx': - fpath[2] = 'Python NetworkX' - elif fpath[2] == 'classification': - fpath[2] = 'Text Classification' - - if case == 0 or case == 'comment-fail': - html = """ - - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection has been terminated.

      -

      We are using the id and permalink from your Reddit Submission dataset - to collect comments and replies. It is most likely you have provide an incomplete Reddit Submission dataset missing these two fields.

      -

      Please try to reproduce the Reddit Submission with id and permalink, or switch to another dataset.

      - Go to your session... -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - - """ - subject = 'Your Reddit Comment collection has failed...' - elif case == 1 or case == 'comment-terminate': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is exceeding 400 Megabyte, and is terminated due to lack of disk space.

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. The partial comments we manage to collect and save will be compressed for you in an .zip file named """ + fpath[3] + """-comments.zip (click)
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
          -
          -

          Best Regards,

          -

          Social Media Macroscope - SMILE

          -
      - - """ - subject = 'Your Reddit Comment collection has been terminated...' - elif case == 2 or case == 'comment-success': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is ready for you!

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. It will be compressed for you in an .zip file named """+ fpath[3] +"""-comments.zip
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] +"""
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your Reddit Comment collection is completed!' - elif case == 3 or case == 'analytics-success': - list_html = '' - for key in links.keys(): - list_html += '
    • ' + key + '
      • ' - - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your """ + fpath[2] + """ results are ready for you! (job ID: """ + fpath[3] + """)

      -
        -
      • You can view the visualization and download the results at HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """ tab
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click view
          • -
        -
        -
      • You can also click the link below to download part of the results: -
          """ + list_html + """
        -
        • -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your ' + fpath[2] + ' computation is completed!' - - msg = MIMEMultipart('alternative') - msg['Subject'] = subject - msg['From'] = fromaddr - msg['To'] = toaddr - msg.attach(MIMEText(html, 'html')) - - server = smtplib.SMTP_SSL(host, port) - server.login(fromaddr, password) - server.sendmail(fromaddr, toaddr, msg.as_string()) - server.quit() - else: - print("Invalid Email host setting! Skip notification.") diff --git a/sentiment_analysis/plot.py b/sentiment_analysis/plot.py deleted file mode 100644 index 431d2ff..0000000 --- a/sentiment_analysis/plot.py +++ /dev/null @@ -1,157 +0,0 @@ -from plotly.graph_objs import * -from plotly.offline import plot -import networkx as nx - - -def plot_pie_chart(labels, values, title): - """ - plot pie chart - :param labels: list of label, shape must match parameter values - :param values: list of values, shape must match parameter labels - :param title: title to show - :return: html code in a div - """ - trace = Pie(labels=labels, values=values, textinfo='label+percent') - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_network(graph, layout, relationships, title): - """ - plot network graph - :param graph: networkx graph - :param layout: network layout - :param relationships: reply, retweet, mention or anything else - :param title: title to show - :return: html code in a div - """ - - if layout == 'spring': - pos = nx.spring_layout(graph) - elif layout == 'circular': - pos = nx.circular_layout(graph) - elif layout == 'fruchterman': - pos = nx.fruchterman_reingold_layout(graph) - elif layout == 'random': - pos = nx.random_layout(graph) - elif layout == 'shell': - pos = nx.shell_layout(graph) - elif layout == 'spectral': - pos = nx.spectral_layout(graph) - edge_attri = nx.get_edge_attributes(graph, 'text') - edge_trace = Scatter(x=[], y=[], text=[], - line=Line(width=1, color='#b5b5b5'), - hoverinfo='text', - mode='lines', - hoveron='points') - for edge in graph.edges(): - x0, y0 = pos[edge[0]] - x1, y1 = pos[edge[1]] - edge_trace['x'] += [x0, x1, None] - edge_trace['y'] += [y0, y1, None] - edge_trace['text'].append(edge_attri[edge]) - - node_trace = Scatter(x=[], y=[], text=[], mode='markers', - hoverinfo='text', hoveron='points+fills', - marker=Marker(showscale=True, - colorscale='Portland', - reversescale=False, - color=[], - size=10, - colorbar=dict(thickness=15, - title='node in-degree plus out-degree', - xanchor='left', - titleside='right'), - line=dict(width=2))) - for node in graph.nodes(): - x, y = pos[node] - node_trace['x'].append(x) - node_trace['y'].append(y) - - # set label - for node in graph.nodes(): - node_trace['marker']['color'].append(graph.in_degree()[node] + graph.out_degree()[node]) - if relationships == 'reply_to': - node_trace['text'].append("@" + node + " is replied by " - + str(graph.in_degree()[node]) - + " user(s), and replies to " - + str(graph.out_degree()[node]) + " user(s)") - - elif relationships == 'retweet_from': - node_trace['text'].append("@" + node + " is retweeted by " - + str(graph.in_degree()[node]) - + " user(s) and retweets from " - + str(graph.out_degree()[node]) - + " user(s)") - - elif relationships == 'mentions': - node_trace['text'].append("@" + node + " is mentioned by " - + str(graph.in_degree()[node]) - + " user(s) and mentions " - + str(graph.out_degree()[node]) - + " user(s)") - - fig = Figure(data=Data([edge_trace, node_trace]), - layout=Layout( - title=title, - titlefont=dict(size=16), showlegend=False, - hovermode='closest', margin=dict(b=20, l=5, r=5, t=40), - xaxis=XAxis(showgrid=False, zeroline=False, - showticklabels=False), - yaxis=YAxis(showgrid=False, zeroline=False, - showticklabels=False) - )) - - div = plot(fig, output_type='div', image='png', auto_open=False, - image_filename='plot_img') - - return div - - -def plot_bar_chart(index, counts, title): - """ - plot bar chart - :param index: x - axis, usually the index - :param counts: y - axis, usually counts - :param title: - :return: - """ - - trace = Bar(x=index, y=counts, - marker=dict(color='rgba(200,75,73,1.0)', - line=dict(color='rgba(111,11,9,1.0)',width=2))) - layout = Layout( - title=title, - font=dict(family='Arial', size=12), - yaxis=dict( - showgrid=True, - showline=True, - showticklabels=True, - linecolor='rgba(102, 102, 102, 0.8)', - linewidth=2 - ), - margin=dict( - l=70, - r=70, - t=70, - b=70, - ) - ) - - fig = Figure(data=[trace], layout=layout) - div = plot(fig, output_type='div', image='png', auto_open=False, image_filename='plot_img') - - return div diff --git a/sentiment_analysis/postToAWSBatch.py b/sentiment_analysis/postToAWSBatch.py deleted file mode 100644 index 73c93f5..0000000 --- a/sentiment_analysis/postToAWSBatch.py +++ /dev/null @@ -1,24 +0,0 @@ -import boto3 -import os - -client = boto3.client('batch', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(jobDefinition, jobName, jobQueue, command): - command.extend(['--HOST_IP', os.environ['HOST_IP'], - '--AWS_ACCESSKEY', os.environ['AWS_ACCESSKEY'], - '--AWS_ACCESSKEYSECRET', os.environ['AWS_ACCESSKEYSECRET'], - '--BUCKET_NAME', os.environ['BUCKET_NAME']]) - - response = client.submit_job( - jobDefinition=jobDefinition, - jobName=jobName, - jobQueue=jobQueue, - containerOverrides={ - 'vcpus': 2, - 'memory': 2048, - 'command': command - } - ) - - return response \ No newline at end of file diff --git a/sentiment_analysis/postToAWSLambda.py b/sentiment_analysis/postToAWSLambda.py deleted file mode 100644 index 8a227e7..0000000 --- a/sentiment_analysis/postToAWSLambda.py +++ /dev/null @@ -1,28 +0,0 @@ -import json -import boto3 -import os - -client = boto3.client('lambda', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(function_name, args): - - # pass information so remote lambda can access local s3 minio - args['HOST_IP'] = os.environ['HOST_IP'] - args['AWS_ACCESSKEY'] = os.environ['AWS_ACCESSKEY'] - args['AWS_ACCESSKEYSECRET'] = os.environ['AWS_ACCESSKEYSECRET'] - args['BUCKET_NAME'] = os.environ['BUCKET_NAME'] - - response = client.invoke( - Payload=json.dumps(args), - FunctionName=function_name, - InvocationType='RequestResponse', - LogType='Tail', - ) - - if response['StatusCode'] == 200: - results = json.loads(response['Payload'].read().decode('utf-8')) - else: - results = {'ERROR': response['FunctionError']} - - return results \ No newline at end of file diff --git a/sentiment_analysis/rabbitmq_handler.py b/sentiment_analysis/rabbitmq_handler.py deleted file mode 100644 index c8d2568..0000000 --- a/sentiment_analysis/rabbitmq_handler.py +++ /dev/null @@ -1,108 +0,0 @@ -import json -import os -import traceback -import logging - -import dataset -import pika -from algorithm import algorithm - -import postToAWSLambda -import postToAWSBatch - - -def rabbitmq_handler(ch, method, properties, body): - try: - msg = {} - - # determine if it goes to aws, lambda, or batch - params = json.loads(body) - - if params['platform'] == 'aws-lambda': - msg = postToAWSLambda.invoke(params['function_name'], params) - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - elif params['platform'] == 'lambda': - path = dataset.organize_path_lambda(params) - - # save the config file - msg['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - msg[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - msg[key] = value - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - elif params['platform'] == 'aws-batch': - postToAWSBatch.invoke(params['jobDefinition'], - params['jobName'], - params['jobQueue'], - params['command']) - - elif params['platform'] == 'batch': - os.system(' '.join(params['command'])) - - else: - raise ValueError( - 'Rabbitmq Message Not Recognizable. ' - 'It has to specify what platform to run: aws-lambda, aws-batch, lambda or batch.') - - except Exception as e: - logging.error(traceback.format_exc()) - msg = {'ERROR': - {'message': str(e), - 'traceback': traceback.format_exc() - } - } - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - return None - - -if __name__ == '__main__': - connection = pika.BlockingConnection(pika.ConnectionParameters( - port=5672, - host="rabbitmq", - heartbeat=600, - blocked_connection_timeout=600 - )) - channel = connection.channel() - - # pass the queue name in environment variable - queue = os.environ['QUEUE_NAME'] - - channel.queue_declare(queue=queue) #, arguments={'x-message-ttl': 1000 * 60 * 10}) # 10 minutes? - channel.basic_qos(prefetch_count=1) - channel.basic_consume(queue=queue, on_message_callback=rabbitmq_handler, auto_ack=True) - channel.start_consuming() diff --git a/sentiment_analysis/requirement.txt b/sentiment_analysis/requirement.txt deleted file mode 100644 index 39ea09a..0000000 --- a/sentiment_analysis/requirement.txt +++ /dev/null @@ -1,10 +0,0 @@ -nltk==3.2.5 -pandas==1.1.3 -plotly==2.7.0 -boto3>=1.6.11 -pika>=1.1.0 -networkx==1.11 -scikit-learn==0.23.2 -scipy==1.5.2 -numpy==1.19.2 -pyclowder==2.7.0 diff --git a/sentiment_analysis/sentiment_analysis.py b/sentiment_analysis/sentiment_analysis.py deleted file mode 100644 index c1ce40c..0000000 --- a/sentiment_analysis/sentiment_analysis.py +++ /dev/null @@ -1,198 +0,0 @@ -import itertools - -from nltk import pos_tag, WordNetLemmatizer -from nltk.corpus import sentiwordnet as swn, stopwords -from nltk.sentiment.vader import SentimentIntensityAnalyzer, \ - allcap_differential, negated -from nltk.tokenize import word_tokenize -import sentiment_analysis_debias as SA_debias -import pickle - - -class Sentiment: - - def __init__(self, df, column): - # user specify which column to; each row is a sentence, get a list of sentences - self.id_column = "id" - if 'id_str' in df.columns: - self.id_column = 'id_str' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - self.sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif 'id' in df.columns: - self.id_column = 'id' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - self.sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif 'comment_id' in df.columns: - self.id_column = 'comment_id' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - self.sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif '_source.id_str' in df.columns: - self.id_column = '_source.id_str' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - self.sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - elif '_source.id' in df.columns: - self.id_column = '_source.id_str' - df_new = df[df[column] != ''][[self.id_column, column]].dropna() - self.sentences = df_new[column].astype('str').tolist() - self.id = df_new[self.id_column].astype('str').tolist() - else: - self.sentences = df[df[column] != ''][column].dropna().astype( - 'str').tolist() - self.id = [] - - # combine sentences into a document - self.text = ''.join(self.sentences) - - def sentiment(self, algorithm='vader'): - ''' - calculate sentence sentiment - and store the list of scores in sentiment.csv - ''' - - sid = SentimentIntensityAnalyzer() - - sentiment_sentence = [ - [self.id_column, 'sentence', 'negative', 'neutral', 'positive', 'compound']] - sentiment_doc = {} - - if algorithm == 'vader': - # sentence level - for sent_id, sent in itertools.zip_longest(self.id, self.sentences): - sent_scores = sid.polarity_scores(sent) - sentiment_sentence.append([sent_id, - sent.encode('utf-8', 'ignore'), - sent_scores['neg'], - sent_scores['neu'], - sent_scores['pos'], - sent_scores['compound']]) - - # document level - sentiment_doc = sid.polarity_scores(self.text) - - elif algorithm == 'sentiWordNet': - doc_pos_score = [] - doc_neg_score = [] - doc_obj_score = [] - - # sentence level - for sent_id, sent in itertools.zip_longest(self.id, self.sentences): - tokens = word_tokenize(sent) - - filtered_tokens = [word.lower() for word in tokens - if (word.isalpha() == True - and word.lower() - not in stopwords.words('english'))] - - wnl = WordNetLemmatizer() - processed_tokens = [wnl.lemmatize(word) for word in - filtered_tokens] - tagged = pos_tag(processed_tokens) - - # convert pos tag to sentiwordnet tag - pos_score = [] - neg_score = [] - obj_score = [] - for tag in tagged: - word = tag[0].lower() - pos = self.pos_short(tag[1]) - - # calculate scores - senti_synset = list(swn.senti_synsets(word, pos)) - - if len(senti_synset) > 0: - # use the most common meaning, 0 - pos_score.append(senti_synset[0].pos_score()) - neg_score.append(senti_synset[0].neg_score()) - obj_score.append(senti_synset[0].obj_score()) - # if valid - if pos_score != [] or neg_score != [] or obj_score != []: - doc_pos_score.append(self.average(pos_score)) - doc_neg_score.append(self.average(neg_score)) - doc_obj_score.append(self.average(obj_score)) - - sentiment_sentence.append([sent_id, - sent.encode('utf-8', 'ignore'), - self.average(neg_score), - self.average(obj_score), - self.average(pos_score), - 'NA']) - - # document level - sentiment_doc = {'neg': self.average(doc_neg_score), - 'neu': self.average(doc_obj_score), - 'pos': self.average(doc_pos_score)} - - elif algorithm == 'debias': - - # load embeddings and model - with open("sentiment_analysis_debias.pickle", "rb") as f: - model = pickle.load(f) - embeddings = SA_debias.load_embeddings('numberbatch-en-17.04b.txt') - - # sentence level - sentiment_sentence = [[self.id_column, 'sentence', 'score']] - doc_score = [] - for sent_id, sent in itertools.zip_longest(self.id, - self.sentences): - # if valid - score = SA_debias.text_to_sentiment(sent, embeddings, model) - if score: - doc_score.append(score) - sentiment_sentence.append( - [sent_id, sent.encode('utf-8', 'ignore'), round(score, 4)]) - else: - sentiment_sentence.append( - [sent_id, sent.encode('utf-8', 'ignore'), 'NA']) - - # document level - sentiment_doc = {'doc': self.average(doc_score)} - - return sentiment_sentence, sentiment_doc - - def negated(self): - ''' - find if a sentence has negation word - store the True/false per sentence to negation.csv - ''' - negation_result = [[self.id_column, 'sentence', 'hasNegation']] - for sent_id, sent in itertools.zip_longest(self.id, self.sentences): - negation_result.append( - [sent_id, sent.encode('utf-8', 'ignore'), negated(sent)]) - - return negation_result - - def allcap(self): - ''' - find if a sentence is composed of all capital letter - store the True/False per sentence to allcap.csv - ''' - allcap_result = [[self.id_column,'sentence', 'ALL CAPITAL']] - for sent_id, sent in itertools.zip_longest(self.id, self.sentences): - allcap_result.append([sent_id, sent.encode('utf-8', 'ignore'), - allcap_differential(sent)]) - - return allcap_result - - ########################################################################## - def average(self, score_list): - """Get arithmetic average of scores.""" - if (score_list): - # round to 4 decimals - return round(sum(score_list) / float(len(score_list)), 4) - - def pos_short(self, pos): - """Convert NLTK POS tags to SWN's POS tags.""" - if pos in set(['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']): - return 'v' - elif pos in set(['JJ', 'JJR', 'JJS']): - return 'a' - elif pos in set(['RB', 'RBR', 'RBS']): - return 'r' - elif pos in set(['NNS', 'NN', 'NNP', 'NNPS']): - return 'n' - else: - return 'a' diff --git a/sentiment_analysis/sentiment_analysis_debias.pickle b/sentiment_analysis/sentiment_analysis_debias.pickle deleted file mode 100644 index d94d885..0000000 Binary files a/sentiment_analysis/sentiment_analysis_debias.pickle and /dev/null differ diff --git a/sentiment_analysis/sentiment_analysis_debias.py b/sentiment_analysis/sentiment_analysis_debias.py deleted file mode 100644 index f2542b2..0000000 --- a/sentiment_analysis/sentiment_analysis_debias.py +++ /dev/null @@ -1,43 +0,0 @@ -import pandas as pd -import re -import numpy as np - - -def load_embeddings(filename): - """ - Load a DataFrame from the generalized text format used by word2vec, GloVe, - fastText, and ConceptNet Numberbatch. The main point where they differ is - whether there is an initial line with the dimensions of the matrix. - """ - labels = [] - rows = [] - with open(filename, encoding='utf-8') as infile: - for i, line in enumerate(infile): - items = line.rstrip().split(' ') - if len(items) == 2: - # This is a header row giving the shape of the matrix - continue - labels.append(items[0]) - values = np.array([float(x) for x in items[1:]], 'f') - rows.append(values) - - arr = np.vstack(rows) - return pd.DataFrame(arr, index=labels, dtype='f') - - -def text_to_sentiment(text, embeddings, model): - # tokenize the sentence - TOKEN_RE = re.compile(r"\w.*?\b") - tokens = [token.casefold() for token in TOKEN_RE.findall(text)] - - # calculate the score - try: - vecs = embeddings.loc[tokens].dropna() - predictions = model.predict_log_proba(vecs) - log_odds = predictions[:, 1] - predictions[:, 0] - sentiments = pd.DataFrame({'sentiment': log_odds}, index=vecs.index) - - return sentiments['sentiment'].mean() - - except: - return None diff --git a/sentiment_analysis/writeToS3.py b/sentiment_analysis/writeToS3.py deleted file mode 100644 index 4fba54f..0000000 --- a/sentiment_analysis/writeToS3.py +++ /dev/null @@ -1,77 +0,0 @@ -import boto3 -import mimetypes -import os -from botocore.client import Config - -client = boto3.client('s3', endpoint_url='http://' + os.environ['HOST_IP'] + ':9000', - aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET'], - config=Config(signature_version='s3v4')) - -bucket_name = os.environ['BUCKET_NAME'] - -def upload(localpath, remotepath, filename): - content_type = mimetypes.guess_type(os.path.join(localpath,filename))[0] - print(filename, content_type) - if content_type == None: - extra_args = {'ContentType':'application/octet-stream'} - else: - extra_args = {'ContentType':content_type} - - client.upload_file(os.path.join(localpath, filename), - bucket_name, - os.path.join(remotepath, filename), - ExtraArgs=extra_args) - - -def createDirectory(DirectoryName): - client.put_object(Bucket=bucket_name, Key=DirectoryName) - - -def generate_downloads(remotepath, filename): - url = client.generate_presigned_url( - ClientMethod='get_object', - Params={ - 'Bucket': bucket_name, - 'Key': os.path.join(remotepath, filename) - }, - ExpiresIn=604800 # one week - ) - - return url - - -def downloadToDisk(filename, localpath, remotepath): - with open(os.path.join(localpath, filename), 'wb') as f: - client.download_fileobj(bucket_name, - os.path.join(remotepath, filename), f) - - -def getObject(remoteKey): - obj = client.get_object(Bucket=bucket_name, Key=remoteKey) - - -def putObject(body, remoteKey): - # bytes or seekable file-like object - obj = client.put_object(Bucket=bucket_name, - Body=body, Key=remoteKey) - print(obj['Body'].read()) - -def listDir(remoteClass): - objects = client.list_objects(Bucket=bucket_name, - Prefix=remoteClass, - Delimiter='/') - foldernames = [] - for o in objects.get('CommonPrefixes'): - foldernames.append(o.get('Prefix')) - - # only return the list of foldernames - return foldernames - - -def listFiles(foldernames): - objects = client.list_objects(Bucket=bucket_name, - Prefix=foldernames) - - # return rich information about the files - return objects.get('Contents') diff --git a/sentiment_analysis_extractor/CHANGELOG.md b/sentiment_analysis_extractor/CHANGELOG.md new file mode 100644 index 0000000..6e5dad4 --- /dev/null +++ b/sentiment_analysis_extractor/CHANGELOG.md @@ -0,0 +1,16 @@ +# Changelog +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.1.0] - 03-15-2023 + +### Added +- Initial release of the sentiment analysis extractor + + +## [0.1.1] - 10-03-2023 + +### Changed +- Support Clowder V2 [#1](https://github.com/clowder-framework/smm-extractor/issues/1) diff --git a/sentiment_analysis_extractor/README.md b/sentiment_analysis_extractor/README.md new file mode 100644 index 0000000..e69de29 diff --git a/network_analysis/SmmExtractor.py b/sentiment_analysis_extractor/SmmExtractor.py similarity index 95% rename from network_analysis/SmmExtractor.py rename to sentiment_analysis_extractor/SmmExtractor.py index 6aefd2c..d79adf2 100644 --- a/network_analysis/SmmExtractor.py +++ b/sentiment_analysis_extractor/SmmExtractor.py @@ -103,6 +103,7 @@ def process_message(self, connector, host, secret_key, resource, parameters): # execute the algorithm # Parse user parameters to determine which column to analyze userParams = parameters.get('parameters') + output = algorithm(df, userParams) connector.message_process(resource, "Running the algorithm...") @@ -110,14 +111,15 @@ def process_message(self, connector, host, secret_key, resource, parameters): for fname, output_data in output.items(): if fname != 'uid': local_output_path = save_local_output("", fname, output_data) - uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, - local_output_path) connector.message_process(resource, "Saving " + local_output_path + "...") + uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, + local_output_path) + connector.message_process(resource, local_output_path + " saved...") - # write params to metadata + connector.message_process(resource, "Writing metadata...") metadata = self.get_metadata(userParams, 'file', uploaded_file_id, host) pyclowder.files.upload_metadata(connector, host, secret_key, uploaded_file_id, metadata) - connector.message_process(resource, "Writing metadata...") + connector.message_process(resource, "Metadata written...") if __name__ == "__main__": diff --git a/sentiment_analysis_extractor/extractor.dockerfile b/sentiment_analysis_extractor/extractor.dockerfile new file mode 100644 index 0000000..8ed9e29 --- /dev/null +++ b/sentiment_analysis_extractor/extractor.dockerfile @@ -0,0 +1,18 @@ +FROM socialmediamacroscope/sentiment_analysis:latest + +RUN mkdir -p /scripts +WORKDIR /scripts + +COPY ./SmmExtractor.py ./ +COPY ./extractor_info.json ./ +COPY ./requirement.txt ./extractor-requirement.txt + +# Install pyClowder and any other python dependencies +RUN pip3 install --no-cache-dir -r ./extractor-requirement.txt -U + +# Command to be run when container is run +# Can add heartbeat to change the refresh rate +CMD python3 SmmExtractor.py --heartbeat 300 + +ENV MAIN_SCRIPT="SmmExtractor.py" \ + CLOWDER_VERSION=1 diff --git a/sentiment_analysis/extractor_info.json b/sentiment_analysis_extractor/extractor_info.json similarity index 67% rename from sentiment_analysis/extractor_info.json rename to sentiment_analysis_extractor/extractor_info.json index d0fd99b..174a1fd 100644 --- a/sentiment_analysis/extractor_info.json +++ b/sentiment_analysis_extractor/extractor_info.json @@ -1,15 +1,19 @@ { "@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", - "name": "smm.preprocessing.analysis", - "version": "0.8", - "description": "Description on this extractor", + "name": "smm.sentiment.analysis", + "version": "0.1.1", + "description": "Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.", "author": "Wang, Chen ", "contributors": [], "contexts": [{}], "repository": [ { "repType": "git", - "repUrl": "https://github.com/ncsa/standalone-smm-analytics" + "repUrl": "https://github.com/ncsa/standalone-smm-analytics.git" + }, + { + "repType": "git", + "repUrl": "https://github.com/clowder-framework/smm-extractor.git" } ], "process": { diff --git a/sentiment_analysis_extractor/requirement.txt b/sentiment_analysis_extractor/requirement.txt new file mode 100644 index 0000000..d5f92cd --- /dev/null +++ b/sentiment_analysis_extractor/requirement.txt @@ -0,0 +1 @@ +pyclowder==3.0.4 diff --git a/topic_modeling/SmmExtractor.py b/topic_modeling/SmmExtractor.py deleted file mode 100644 index 6aefd2c..0000000 --- a/topic_modeling/SmmExtractor.py +++ /dev/null @@ -1,125 +0,0 @@ -#!/usr/bin/env python - -"""Example extractor based on the clowder code.""" -import pandas as pd -import json -import os -import csv -import types -import pickle - -import logging -from pyclowder.extractors import Extractor -import pyclowder.files - -from algorithm import algorithm - - -def save_local_output(localSavePath, fname, output_data): - """ - save output in memory first to local file - :param localSavePath: local saved file - :param remoteSavePath: remote save file path - :param fname: filename - :param output_data: the actual data - :return: local saved file path - """ - # json - if isinstance(output_data, dict): - fname += '.json' - with open(os.path.join(localSavePath, fname), 'w') as f: - json.dump(output_data, f) - - # dataframe to csv - elif isinstance(output_data, pd.DataFrame): - fname += '.csv' - output_data.to_csv(fname, encoding='utf-8') - - # string to html - elif isinstance(output_data, str): - fname += '.html' - with open(os.path.join(localSavePath, fname), 'w') as f: - f.write(output_data) - - # list(list) to csv - elif isinstance(output_data, list) \ - and (isinstance(output_data[0], list) or isinstance(output_data[0], - tuple)): - fname += '.csv' - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - writer = csv.writer(f) - for row in output_data: - try: - writer.writerow(row) - except UnicodeEncodeError as e: - print(e) - - # special case - elif isinstance(output_data, types.GeneratorType): - if fname == 'gephi': - fname += '.gml' - elif fname == 'pajek': - fname += '.net' - else: - fname += '.unknown' - - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - for line in output_data: - f.write(line + '\n') - - # else pickle the object - else: - fname += '.pickle' - with open(os.path.join(localSavePath, fname), 'wb') as f: - pickle.dump(output_data, f) - - return os.path.join(localSavePath, fname) - - -class SmmExtractor(Extractor): - """Count the number of characters, words and lines in a text file.""" - def __init__(self): - Extractor.__init__(self) - - # parse command line and load default logging configuration - self.setup() - - # setup logging for the exctractor - logging.getLogger('pyclowder').setLevel(logging.DEBUG) - logging.getLogger('__main__').setLevel(logging.DEBUG) - - def process_message(self, connector, host, secret_key, resource, parameters): - # this extractor runs on dataset - # uncomment to see the resource - logger = logging.getLogger(__name__) - inputfile = resource["local_paths"][0] - dataset_id = resource['parent'].get('id') - - df = pd.read_csv(inputfile) - connector.message_process(resource, "Loading contents of file...") - - # execute the algorithm - # Parse user parameters to determine which column to analyze - userParams = parameters.get('parameters') - output = algorithm(df, userParams) - connector.message_process(resource, "Running the algorithm...") - - # upload object to s3 bucket and return the url - for fname, output_data in output.items(): - if fname != 'uid': - local_output_path = save_local_output("", fname, output_data) - uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, - local_output_path) - connector.message_process(resource, "Saving " + local_output_path + "...") - - # write params to metadata - metadata = self.get_metadata(userParams, 'file', uploaded_file_id, host) - pyclowder.files.upload_metadata(connector, host, secret_key, uploaded_file_id, metadata) - connector.message_process(resource, "Writing metadata...") - - -if __name__ == "__main__": - extractor = SmmExtractor() - extractor.start() diff --git a/topic_modeling/algorithm.py b/topic_modeling/algorithm.py deleted file mode 100644 index 5b98ce8..0000000 --- a/topic_modeling/algorithm.py +++ /dev/null @@ -1,52 +0,0 @@ -import pandas as pd -from gensim_topic_modeling import Gensim_Topic_Modeling - - -def algorithm(df, params): - """ - wrapper function to put each individual algorithm inside - :param df: dataframe that contains all the input dataset - :param params: algorithm specific parameters - :return: a dictionary of { outputname: output content in memory } - """ - - output = {} - - gensim_tm = Gensim_Topic_Modeling(df, column=params["column"]) - data_lemmatized, id2word, corpus = gensim_tm.preprocessing() - output['lemmatized'] = data_lemmatized - - lda_model = gensim_tm.build_lda_model(int(params['numTopics']), corpus, id2word) - output['lda_model'] = lda_model - - metrics = gensim_tm.lda_model_metrics( - lda_model, corpus, id2word, data_lemmatized) - output['metrics'] = metrics - - html = gensim_tm.visualize_lda_model(lda_model, corpus, id2word) - output['div'] = html - - return output - - -if __name__ == '__main__': - """ - help user with no access to AWS test their model - to test just run algorithm.py: - python3 algorithm.py - """ - - # download our example dataset and place it under the same directory of this script - df = pd.read_csv('example_dataset.csv') - - # add your parameters needed by the analysis - params = { - "column": "text", - "numTopics": 5, - } - - # execute your algorithm - output = algorithm(df, params) - - # see if the outputs are what you desired - print(output['metrics'], output['div']) diff --git a/topic_modeling/batch_function.py b/topic_modeling/batch_function.py deleted file mode 100644 index 63ba1ea..0000000 --- a/topic_modeling/batch_function.py +++ /dev/null @@ -1,58 +0,0 @@ -import dataset -import argparse -from notification import notification -from algorithm import algorithm - -if __name__ == '__main__': - - # entrance to invoke Batch - urls = {} - - # default parameters - parser = argparse.ArgumentParser(description="processing...") - parser.add_argument('--remoteReadPath', required=True) - parser.add_argument('--column', required=True) - parser.add_argument('--s3FolderName', required=True) - parser.add_argument('--uid', required=True) - parser.add_argument('--resultPath', required=True) - parser.add_argument('--email', required=True) - parser.add_argument('--sessionURL', required=True) - - # user specified parameters - parsed, unknown = parser.parse_known_args() - for arg in unknown: - if arg.startswith("--"): - parser.add_argument(arg, required=False) - - params = vars(parser.parse_args()) - - # arranging the paths - path = dataset.organize_path_lambda(params) - - # save the config file - urls['config'] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - 'config', - params) - - # prepare input dataset - df = dataset.get_remote_input(path['remoteReadPath'], - path['filename'], - path['localReadPath']) - - # execute the algorithm - output = algorithm(df, params) - - # upload object to s3 bucket and return the url - for key, value in output.items(): - if key != 'uid': - urls[key] = dataset.save_remote_output(path['localSavePath'], - path['remoteSavePath'], - key, - value) - else: - urls[key] = value - - # push notification email - notification(toaddr=params['email'], case=3, filename=path['remoteSavePath'], - links=urls, sessionURL=params['sessionURL']) diff --git a/topic_modeling/clear_cache.sh b/topic_modeling/clear_cache.sh deleted file mode 100644 index 640c2ac..0000000 --- a/topic_modeling/clear_cache.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env bash -CACHEDIR=/tmp - -# delete file older than 4 hours -find "$CACHEDIR" -maxdepth 1 -mindepth 1 -type d -mmin +240 -print0 | - while IFS= read -d '' -r dir; do - rm -rf "$dir" - done \ No newline at end of file diff --git a/topic_modeling/clear_cache_cron b/topic_modeling/clear_cache_cron deleted file mode 100644 index 0510c51..0000000 --- a/topic_modeling/clear_cache_cron +++ /dev/null @@ -1 +0,0 @@ -0 * * * * /bin/bash /scripts/clear_cache.sh diff --git a/topic_modeling/command.txt b/topic_modeling/command.txt deleted file mode 100644 index 5fdc30e..0000000 --- a/topic_modeling/command.txt +++ /dev/null @@ -1,3 +0,0 @@ -docker build -t socialmediamacroscope/topic_modeling:latest . -docker build -f extractor.dockerfile -t socialmediamacroscope/topic_modeling_extractor:latest . -docker push socialmediamacroscope/topic_modeling_extractor:latest diff --git a/topic_modeling/dataset.py b/topic_modeling/dataset.py deleted file mode 100644 index c1bb7b5..0000000 --- a/topic_modeling/dataset.py +++ /dev/null @@ -1,148 +0,0 @@ -import csv -import os -import json -import pickle -import pandas as pd -import types -import writeToS3 as s3 - - -def organize_path_lambda(event): - """ - parse the lambda handler parameter event to construct necessary paths for reading and storing data - :param event: aws lambda parameters from handler - :return: path dictionary - """ - # arranging the paths - localReadPath = os.path.join('/tmp', event['s3FolderName'], event['uid']) - localSavePath = os.path.join('/tmp', - event['s3FolderName'] + event['resultPath'], - event['uid']) - remoteSavePath = os.path.join(event['s3FolderName'] + event['resultPath'], - event['uid']) - if 'remoteReadPath' not in event.keys() or event['remoteReadPath'] == 'undefined': - remoteReadPath = remoteSavePath - filename = event['labeledFilename'] - else: - remoteReadPath = event['remoteReadPath'] - filename = remoteReadPath.split('/')[-2] + '.csv' - - if not os.path.exists(localSavePath): - os.makedirs(localSavePath) - if not os.path.exists(localReadPath): - os.makedirs(localReadPath) - - path = { - 'remoteReadPath': remoteReadPath, - 'localReadPath': localReadPath, - 'localSavePath': localSavePath, - 'remoteSavePath': remoteSavePath, - 'filename': filename - } - - return path - - -def get_remote_input(remoteReadPath, filename, localReadPath): - """ - download input file from s3 bucket to a local location, and then load - it to a pandas dataframe - :param remoteReadPath: remote path in s3 to store the data - :param localReadPath: local location to store the data, usually in /tmp - :return: df: dataframe that contains the complete input file - """ - s3.downloadToDisk(filename, localReadPath, remoteReadPath) - - # quick fix for decoding error, sometimes the data is coded in ISO-8859-1 - # Array = 2D nested list holding column and row data - Array = [] - try: - with open(os.path.join(localReadPath, filename), 'r', - encoding='utf-8', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - except Exception: - with open(os.path.join(localReadPath, filename), 'r', - encoding='ISO-8859-1', errors="ignore") as f: - reader = csv.reader(f) - try: - for row in reader: - Array.append(row) - except Exception as e: - print(e) - - # load to pandas dataframe - df = pd.DataFrame(Array[1:], columns=Array[0]) - - return df - - -def save_remote_output(localSavePath, remoteSavePath, fname, output_data): - """ - save output in memory first to local file, then upload to remote S3 bucket - :param localSavePath: local saved file - :param remoteSavePath: remote save file path - :param fname: filename - :param output_data: the actual data - :return: url of the file saved in S3 bucket - """ - - # json - if isinstance(output_data, dict): - fname += '.json' - with open(os.path.join(localSavePath, fname), 'w') as f: - json.dump(output_data, f) - - # dataframe to csv - elif isinstance(output_data, pd.DataFrame): - fname += '.csv' - output_data.to_csv(fname, encoding='utf-8') - - # string to html - elif isinstance(output_data, str): - fname += '.html' - with open(os.path.join(localSavePath, fname), 'w') as f: - f.write(output_data) - - # list(list) to csv - elif isinstance(output_data, list) \ - and (isinstance(output_data[0], list) or isinstance(output_data[0], - tuple)): - fname += '.csv' - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - writer = csv.writer(f) - for row in output_data: - try: - writer.writerow(row) - except UnicodeEncodeError as e: - print(e) - - # special case - elif isinstance(output_data, types.GeneratorType): - if fname == 'gephi': - fname += '.gml' - elif fname == 'pajek': - fname += '.net' - else: - fname += '.unknown' - - with open(os.path.join(localSavePath, fname), 'w', newline='', - encoding='utf-8') as f: - for line in output_data: - f.write(line + '\n') - - # else pickle the object - else: - fname += '.pickle' - with open(os.path.join(localSavePath, fname), 'wb') as f: - pickle.dump(output_data, f) - - s3.upload(localSavePath, remoteSavePath, fname) - url = s3.generate_downloads(remoteSavePath, fname) - - return url diff --git a/topic_modeling/dockerfile b/topic_modeling/dockerfile deleted file mode 100644 index f4eff79..0000000 --- a/topic_modeling/dockerfile +++ /dev/null @@ -1,18 +0,0 @@ -FROM python:3.7 - -RUN apt-get -qq -y update && apt-get -qq -y install cron - -RUN mkdir -p /scripts -WORKDIR /scripts - -# copy paste python scripts -COPY . ./ - -# install dependency libraries and download required data -RUN pip install --no-cache-dir -r requirement.txt -RUN python3 -m nltk.downloader -d /usr/local/share/nltk_data stopwords wordnet - -# cron job clean tmp folder -RUN chmod u+x ./clear_cache.sh -RUN chmod 0644 ./clear_cache_cron -RUN crontab ./clear_cache_cron \ No newline at end of file diff --git a/topic_modeling/extractor.dockerfile b/topic_modeling/extractor.dockerfile deleted file mode 100644 index 14c201b..0000000 --- a/topic_modeling/extractor.dockerfile +++ /dev/null @@ -1,19 +0,0 @@ -FROM python:3.8.5 - -RUN mkdir -p /scripts -WORKDIR /scripts - -COPY . ./ - -# Install pyClowder and any other python dependencies -RUN pip install --no-cache-dir -r requirement.txt -RUN python3 -m nltk.downloader -d /usr/local/share/nltk_data stopwords wordnet - -# wordnet cannot unzip fix -RUN unzip /usr/local/share/nltk_data/corpora/wordnet.zip -d /usr/local/share/nltk_data/corpora - -# Command to be run when container is run -# Can add heartbeat to change the refresh rate -CMD python3 SmmExtractor.py --heartbeat 40 - -ENV MAIN_SCRIPT="SmmExtractor.py" diff --git a/topic_modeling/gensim_topic_modeling.py b/topic_modeling/gensim_topic_modeling.py deleted file mode 100644 index 356b573..0000000 --- a/topic_modeling/gensim_topic_modeling.py +++ /dev/null @@ -1,103 +0,0 @@ -from nltk.corpus import stopwords -import re -import gensim -import gensim.corpora as corpora -from gensim.utils import simple_preprocess -from gensim.models import CoherenceModel -from nltk import WordNetLemmatizer -import pyLDAvis -import pyLDAvis.gensim - - -class Gensim_Topic_Modeling: - - def __init__(self, df, column): - self.data = df[df[column] != ''][column].dropna().astype( - 'str').tolist() - - def preprocessing(self): - self.data = [re.sub('\S*@\S*\s?', "", sent) for sent in self.data] - self.data = [re.sub('\s+', ' ', sent) for sent in self.data] - self.data = [re.sub("\'", "", sent) for sent in self.data] - self.data = [re.sub(r"http\S+", "", sent) for sent in self.data] - self.data = [re.sub(r"rt\S+", "", sent) for sent in self.data] - - data_words = list(self.sent_to_words(self.data)) - - # Build the bigram - bigram = gensim.models.Phrases(data_words, min_count=5, threshold=50) - # higher threshold fewer phrases. - - # Remove Stop Words - data_words_nostops = self.remove_stopwords(data_words) - - # Form Bigrams - bigram_mod = gensim.models.phrases.Phraser(bigram) - data_words_bigrams = self.make_bigrams(bigram_mod, data_words_nostops) - - # lemmatization keeping only noun, adj, vb, adv - data_lemmatized = self.lemmatization(data_words_bigrams) - - # Create Dictionary - id2word = corpora.Dictionary(data_lemmatized) - - # Term Document Frequency - corpus = [id2word.doc2bow(text) for text in data_lemmatized] - - return data_lemmatized, id2word, corpus - - def build_lda_model(self, num_topics, corpus, id2word): - lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, - id2word=id2word, - num_topics=num_topics, - random_state=100, - update_every=1, - chunksize=100, - passes=10, - alpha='auto', - per_word_topics=True) - - return lda_model - - def lda_model_metrics(self, lda_model, corpus, id2word, data_lemmatized): - perplexity = lda_model.log_perplexity(corpus) - coherence_model_lda = CoherenceModel(model=lda_model, - texts=data_lemmatized, - dictionary=id2word, - coherence='c_v') - coherence_lda = coherence_model_lda.get_coherence() - metrics = [["perplexity", "coherence score"], - [perplexity, coherence_lda]] - - return metrics - - def visualize_lda_model(self, lda_model, corpus, id2word): - data = pyLDAvis.gensim.prepare(lda_model, corpus, id2word) - html = pyLDAvis.prepared_data_to_html(data) - - return html - - @staticmethod - def sent_to_words(sentences): - for sentence in sentences: - yield (gensim.utils.simple_preprocess(str(sentence), deacc=True)) - # deacc=True removes punctuations - - @staticmethod - def remove_stopwords(texts): - stop_words = stopwords.words('english') - return [[word for word in simple_preprocess(str(doc)) if - word not in stop_words] for doc in texts] - - @staticmethod - def make_bigrams(bigram_mod, texts): - return [bigram_mod[doc] for doc in texts] - - @staticmethod - def lemmatization(texts): - texts_out = [] - wnl = WordNetLemmatizer() - for sent in texts: - texts_out.append([wnl.lemmatize(t) for t in sent]) - - return texts_out diff --git a/topic_modeling/notification.py b/topic_modeling/notification.py deleted file mode 100644 index 176e095..0000000 --- a/topic_modeling/notification.py +++ /dev/null @@ -1,170 +0,0 @@ -import smtplib -from email.mime.multipart import MIMEMultipart -from email.mime.text import MIMEText -import os - - -def notification(toaddr,case,filename,links,sessionURL): - # toaddr -- email address to send to - # text content to send - # subject - host = os.environ.get('EMAIL_HOST') - port = os.environ.get('EMAIL_PORT') - fromaddr = os.environ.get('EMAIL_FROM_ADDRESS') - password = os.environ.get('EMAIL_PASSWORD') - - if host is not None and host != "" and \ - port is not None and port != "" and\ - fromaddr is not None and fromaddr != "" and \ - password is not None and password != "": - # map the fpath component to History panel names - # local/NLP/sentiment/xxxxxxxxxxxxxxxxxxxxxxxx/ => [local,nlp,sentiment,xxxx,space] - # [local, GraphQL, reddit-post, aww, space] - # 0 1 2 3 - if filename != '': - fpath = filename.split('/') - - if fpath[1] == 'GraphQL': - fpath[1] = 'Social Media Data' - elif fpath[1] == 'NLP': - fpath[1] = 'Nature Language Processing' - elif fpath[1] == 'ML': - fpath[1] = 'Machine Learning ML' - elif fpath[1] == 'NW': - fpath[1] = 'Network Visualization and Analysis' - - if fpath[2] == 'reddit-Post': - fpath[2] = 'Subreddit Posts Title' - elif fpath[2] == 'reddit-Historical-Post': - fpath[2] = 'Reddit Historical Post' - elif fpath[2] == 'reddit-Search': - fpath[2] = 'Reddit Search Posts Title' - elif fpath[2] == 'sentiment': - fpath[2] = 'Sentiment Analysis' - elif fpath[2] == 'preprocessing': - fpath[2] = 'NLP Preprocessing' - elif fpath[2] == 'networkx': - fpath[2] = 'Python NetworkX' - elif fpath[2] == 'classification': - fpath[2] = 'Text Classification' - - if case == 0 or case == 'comment-fail': - html = """ - - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection has been terminated.

      -

      We are using the id and permalink from your Reddit Submission dataset - to collect comments and replies. It is most likely you have provide an incomplete Reddit Submission dataset missing these two fields.

      -

      Please try to reproduce the Reddit Submission with id and permalink, or switch to another dataset.

      - Go to your session... -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - - """ - subject = 'Your Reddit Comment collection has failed...' - elif case == 1 or case == 'comment-terminate': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is exceeding 400 Megabyte, and is terminated due to lack of disk space.

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. The partial comments we manage to collect and save will be compressed for you in an .zip file named """ + fpath[3] + """-comments.zip (click)
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
          -
          -

          Best Regards,

          -

          Social Media Macroscope - SMILE

          -
      - - """ - subject = 'Your Reddit Comment collection has been terminated...' - elif case == 2 or case == 'comment-success': - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your Reddit Comment collection is ready for you!

      -
        -
      • You have requested comments and replies for the Reddit Submission (Post):""" + fpath[3] + """. It will be compressed for you in an .zip file named """+ fpath[3] +"""-comments.zip
        • -
      • In order to download this file, you need to first locate the original submission in the HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] +"""
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click VIEW
          • -
        • --> in the Overview table under the downloadables column, you will find these comments in a zip file.
          • -
        -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your Reddit Comment collection is completed!' - elif case == 3 or case == 'analytics-success': - list_html = '' - for key in links.keys(): - list_html += '
    • ' + key + '
      • ' - - html = """ - - -
      -

      Dear user (session ID: """ + fpath[0] + """),

      -

      Your """ + fpath[2] + """ results are ready for you! (job ID: """ + fpath[3] + """)

      -
        -
      • You can view the visualization and download the results at HISTORY page in SMILE. - Go to your session... -
          -
        • Go to History
          • -
        • --> under """ + fpath[1] + """ tab
          • -
        • --> click """ + fpath[2] + """
          • -
        • --> then find """ + fpath[3] + """
          • -
        • --> click view
          • -
        -
        -
      • You can also click the link below to download part of the results: -
          """ + list_html + """
        -
        • -
      -
      -

      Best Regards,

      -

      Social Media Macroscope - SMILE

      -
      - - """ - subject = 'Your ' + fpath[2] + ' computation is completed!' - - msg = MIMEMultipart('alternative') - msg['Subject'] = subject - msg['From'] = fromaddr - msg['To'] = toaddr - msg.attach(MIMEText(html, 'html')) - - server = smtplib.SMTP_SSL(host, port) - server.login(fromaddr, password) - server.sendmail(fromaddr, toaddr, msg.as_string()) - server.quit() - else: - print("Invalid Email host setting! Skip notification.") diff --git a/topic_modeling/postToAWSBatch.py b/topic_modeling/postToAWSBatch.py deleted file mode 100644 index c24a9a5..0000000 --- a/topic_modeling/postToAWSBatch.py +++ /dev/null @@ -1,24 +0,0 @@ -import os - -import boto3 - -client = boto3.client('batch', region_name="us-west-2", aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET']) - -def invoke(jobDefinition, jobName, jobQueue, command): - command.extend(['--HOST_IP', os.environ['HOST_IP'], - '--AWS_ACCESSKEY', os.environ['AWS_ACCESSKEY'], - '--AWS_ACCESSKEYSECRET', os.environ['AWS_ACCESSKEYSECRET'], - '--BUCKET_NAME', os.environ['BUCKET_NAME']]) - response = client.submit_job( - jobDefinition=jobDefinition, - jobName=jobName, - jobQueue=jobQueue, - containerOverrides={ - 'vcpus': 2, - 'memory': 2048, - 'command': command - } - ) - - return response \ No newline at end of file diff --git a/topic_modeling/rabbitmq_handler.py b/topic_modeling/rabbitmq_handler.py deleted file mode 100644 index 710d574..0000000 --- a/topic_modeling/rabbitmq_handler.py +++ /dev/null @@ -1,63 +0,0 @@ -import json -import os -import traceback -import pika -import postToAWSBatch - - -def rabbitmq_handler(ch, method, properties, body): - try: - msg = {} - - # determine if it goes to aws, lambda, or batch - params = json.loads(body) - - if params['platform'] == 'aws-lambda': - raise ValueError("Not applicable to this algorithm.") - - elif params['platform'] == 'lambda': - raise ValueError("Not applicable to this algorithm.") - - elif params['platform'] == 'aws-batch': - postToAWSBatch.invoke(params['jobDefinition'], - params['jobName'], - params['jobQueue'], - params['command']) - - - elif params['platform'] == 'batch': - os.system(' '.join(params['command'])) - - else: - raise ValueError( - 'Rabbitmq Message Not Recognizable. ' - 'It has to specify what platform to run: aws-lambda, aws-batch, lambda or batch.') - - except BaseException as e: - - msg = {'ERROR': - {'message': str(e), - 'traceback': traceback.format_exc() - } - } - - # reply to the sender - ch.basic_publish(exchange="", - routing_key=properties.reply_to, - properties=pika.BasicProperties(correlation_id=properties.correlation_id), - body=json.dumps(msg)) - - return None - - -if __name__ == '__main__': - connection = pika.BlockingConnection(pika.ConnectionParameters(port=5672, host="rabbitmq")) - channel = connection.channel() - - # pass the queue name in environment variable - queue = os.environ['QUEUE_NAME'] - - channel.queue_declare(queue=queue) - channel.basic_qos(prefetch_count=1) - channel.basic_consume(queue=queue, on_message_callback=rabbitmq_handler, auto_ack=True) - channel.start_consuming() diff --git a/topic_modeling/requirement.txt b/topic_modeling/requirement.txt deleted file mode 100644 index 02c4398..0000000 --- a/topic_modeling/requirement.txt +++ /dev/null @@ -1,8 +0,0 @@ -nltk>=3.4.4 -gensim==3.7.1 -boto3>=1.6.11 -numpy>=1.18.1 -pandas>=1.1.4 -pyLDAvis==2.1.2 -pika>=1.1.0 -pyclowder==2.7.0 diff --git a/topic_modeling/writeToS3.py b/topic_modeling/writeToS3.py deleted file mode 100644 index 4fba54f..0000000 --- a/topic_modeling/writeToS3.py +++ /dev/null @@ -1,77 +0,0 @@ -import boto3 -import mimetypes -import os -from botocore.client import Config - -client = boto3.client('s3', endpoint_url='http://' + os.environ['HOST_IP'] + ':9000', - aws_access_key_id = os.environ['AWS_ACCESSKEY'], - aws_secret_access_key = os.environ['AWS_ACCESSKEYSECRET'], - config=Config(signature_version='s3v4')) - -bucket_name = os.environ['BUCKET_NAME'] - -def upload(localpath, remotepath, filename): - content_type = mimetypes.guess_type(os.path.join(localpath,filename))[0] - print(filename, content_type) - if content_type == None: - extra_args = {'ContentType':'application/octet-stream'} - else: - extra_args = {'ContentType':content_type} - - client.upload_file(os.path.join(localpath, filename), - bucket_name, - os.path.join(remotepath, filename), - ExtraArgs=extra_args) - - -def createDirectory(DirectoryName): - client.put_object(Bucket=bucket_name, Key=DirectoryName) - - -def generate_downloads(remotepath, filename): - url = client.generate_presigned_url( - ClientMethod='get_object', - Params={ - 'Bucket': bucket_name, - 'Key': os.path.join(remotepath, filename) - }, - ExpiresIn=604800 # one week - ) - - return url - - -def downloadToDisk(filename, localpath, remotepath): - with open(os.path.join(localpath, filename), 'wb') as f: - client.download_fileobj(bucket_name, - os.path.join(remotepath, filename), f) - - -def getObject(remoteKey): - obj = client.get_object(Bucket=bucket_name, Key=remoteKey) - - -def putObject(body, remoteKey): - # bytes or seekable file-like object - obj = client.put_object(Bucket=bucket_name, - Body=body, Key=remoteKey) - print(obj['Body'].read()) - -def listDir(remoteClass): - objects = client.list_objects(Bucket=bucket_name, - Prefix=remoteClass, - Delimiter='/') - foldernames = [] - for o in objects.get('CommonPrefixes'): - foldernames.append(o.get('Prefix')) - - # only return the list of foldernames - return foldernames - - -def listFiles(foldernames): - objects = client.list_objects(Bucket=bucket_name, - Prefix=foldernames) - - # return rich information about the files - return objects.get('Contents') diff --git a/topic_modeling_extractor/CHANGELOG.md b/topic_modeling_extractor/CHANGELOG.md new file mode 100644 index 0000000..0bccccd --- /dev/null +++ b/topic_modeling_extractor/CHANGELOG.md @@ -0,0 +1,16 @@ +# Changelog +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.1.0] - 03-15-2023 + +### Added +- Initial release of the topic modeling extractor + + +## [0.1.1] - 10-03-2023 + +### Changed +- Support Clowder V2 [#1](https://github.com/clowder-framework/smm-extractor/issues/1) diff --git a/topic_modeling_extractor/README.md b/topic_modeling_extractor/README.md new file mode 100644 index 0000000..e69de29 diff --git a/topic_modeling_extractor/SmmExtractor.py b/topic_modeling_extractor/SmmExtractor.py new file mode 100644 index 0000000..d79adf2 --- /dev/null +++ b/topic_modeling_extractor/SmmExtractor.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python + +"""Example extractor based on the clowder code.""" +import pandas as pd +import json +import os +import csv +import types +import pickle + +import logging +from pyclowder.extractors import Extractor +import pyclowder.files + +from algorithm import algorithm + + +def save_local_output(localSavePath, fname, output_data): + """ + save output in memory first to local file + :param localSavePath: local saved file + :param remoteSavePath: remote save file path + :param fname: filename + :param output_data: the actual data + :return: local saved file path + """ + # json + if isinstance(output_data, dict): + fname += '.json' + with open(os.path.join(localSavePath, fname), 'w') as f: + json.dump(output_data, f) + + # dataframe to csv + elif isinstance(output_data, pd.DataFrame): + fname += '.csv' + output_data.to_csv(fname, encoding='utf-8') + + # string to html + elif isinstance(output_data, str): + fname += '.html' + with open(os.path.join(localSavePath, fname), 'w') as f: + f.write(output_data) + + # list(list) to csv + elif isinstance(output_data, list) \ + and (isinstance(output_data[0], list) or isinstance(output_data[0], + tuple)): + fname += '.csv' + with open(os.path.join(localSavePath, fname), 'w', newline='', + encoding='utf-8') as f: + writer = csv.writer(f) + for row in output_data: + try: + writer.writerow(row) + except UnicodeEncodeError as e: + print(e) + + # special case + elif isinstance(output_data, types.GeneratorType): + if fname == 'gephi': + fname += '.gml' + elif fname == 'pajek': + fname += '.net' + else: + fname += '.unknown' + + with open(os.path.join(localSavePath, fname), 'w', newline='', + encoding='utf-8') as f: + for line in output_data: + f.write(line + '\n') + + # else pickle the object + else: + fname += '.pickle' + with open(os.path.join(localSavePath, fname), 'wb') as f: + pickle.dump(output_data, f) + + return os.path.join(localSavePath, fname) + + +class SmmExtractor(Extractor): + """Count the number of characters, words and lines in a text file.""" + def __init__(self): + Extractor.__init__(self) + + # parse command line and load default logging configuration + self.setup() + + # setup logging for the exctractor + logging.getLogger('pyclowder').setLevel(logging.DEBUG) + logging.getLogger('__main__').setLevel(logging.DEBUG) + + def process_message(self, connector, host, secret_key, resource, parameters): + # this extractor runs on dataset + # uncomment to see the resource + logger = logging.getLogger(__name__) + inputfile = resource["local_paths"][0] + dataset_id = resource['parent'].get('id') + + df = pd.read_csv(inputfile) + connector.message_process(resource, "Loading contents of file...") + + # execute the algorithm + # Parse user parameters to determine which column to analyze + userParams = parameters.get('parameters') + + output = algorithm(df, userParams) + connector.message_process(resource, "Running the algorithm...") + + # upload object to s3 bucket and return the url + for fname, output_data in output.items(): + if fname != 'uid': + local_output_path = save_local_output("", fname, output_data) + connector.message_process(resource, "Saving " + local_output_path + "...") + uploaded_file_id = pyclowder.files.upload_to_dataset(connector, host, secret_key, dataset_id, + local_output_path) + connector.message_process(resource, local_output_path + " saved...") + + connector.message_process(resource, "Writing metadata...") + metadata = self.get_metadata(userParams, 'file', uploaded_file_id, host) + pyclowder.files.upload_metadata(connector, host, secret_key, uploaded_file_id, metadata) + connector.message_process(resource, "Metadata written...") + + +if __name__ == "__main__": + extractor = SmmExtractor() + extractor.start() diff --git a/topic_modeling_extractor/extractor.dockerfile b/topic_modeling_extractor/extractor.dockerfile new file mode 100644 index 0000000..2a9be8a --- /dev/null +++ b/topic_modeling_extractor/extractor.dockerfile @@ -0,0 +1,18 @@ +FROM socialmediamacroscope/topic_modeling:latest + +RUN mkdir -p /scripts +WORKDIR /scripts + +COPY ./SmmExtractor.py ./ +COPY ./extractor_info.json ./ +COPY ./requirement.txt ./extractor-requirement.txt + +# Install pyClowder and any other python dependencies +RUN pip3 install --no-cache-dir -r ./extractor-requirement.txt -U + +# Command to be run when container is run +# Can add heartbeat to change the refresh rate +CMD python3 SmmExtractor.py --heartbeat 300 + +ENV MAIN_SCRIPT="SmmExtractor.py" \ + CLOWDER_VERSION=1 diff --git a/topic_modeling/extractor_info.json b/topic_modeling_extractor/extractor_info.json similarity index 56% rename from topic_modeling/extractor_info.json rename to topic_modeling_extractor/extractor_info.json index 4363476..40532d0 100644 --- a/topic_modeling/extractor_info.json +++ b/topic_modeling_extractor/extractor_info.json @@ -1,15 +1,19 @@ { "@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", "name": "smm.topic.modeling", - "version": "0.1", - "description": "Description on this extractor", + "version": "0.1.1", + "description": "One of the primary applications of natural language processing is to automatically extract what topics people are discussing from large volumes of text. Topic modeling is a type of statistical modeling for discovering the abstract topics that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.", "author": "Wang, Chen ", "contributors": [], "contexts": [{}], "repository": [ { "repType": "git", - "repUrl": "https://github.com/ncsa/standalone-smm-analytics" + "repUrl": "https://github.com/ncsa/standalone-smm-analytics.git" + }, + { + "repType": "git", + "repUrl": "https://github.com/clowder-framework/smm-extractor.git" } ], "process": { diff --git a/topic_modeling_extractor/requirement.txt b/topic_modeling_extractor/requirement.txt new file mode 100644 index 0000000..d5f92cd --- /dev/null +++ b/topic_modeling_extractor/requirement.txt @@ -0,0 +1 @@ +pyclowder==3.0.4