Link Youtube : https://youtu.be/dPVjAJGEgsI
The model aims to predict public response to government policies based on someone's tweet. The research conducted aims to build a sentiment analysis pros and cons that can be used in considering new policies. The model will accept input from Twitter users that will be grouped into two classes, namely a positive class and a negative class.
Data Science 01
- Ghinaa Zain Nabiilah
- Meidy Tataluckyta
- Putri Apriyanti Windya
- Rizky Nur Alfian
from crawling process by filtering Tweet on certain keywords related to government policies such as "vaccine" that related to government policies regarding vaccines.
Dataset link: https://raw.githubusercontent.com/PutriAW/Predictions-of-Public-Response-to-Indonesian-Government-Policies/main/Dataset/raw%20dataset.csv
This how the dataset looks like in wordcloud :
- Indonesian language text is preferred.
-
Case Folding, The process of converting all the characters in a document into the same case
- removal of @name [mention]
- removal of links [https://blabala.com]
- removal of RT
- removal of punctuations and numbers
- remove whitespace
- convert text to Lowercase
-
Tokenization, the act of breaking up a sequence of strings into pieces such as words called tokens.
-
Stopword Removal, is a step that can clean data from words that are not unique words, such as conjunctions or other adverbs.
-
Stemming is a process that can clean data from affixes, prefixes, greetings, suffixes, or combinations.
-
Slang Removal
Neural networks are forecasting methods that are based on simple mathematical models of the brain. They allow complex nonlinear relationships between the response variable and its predictors.
- On this project we used three data classifications SVM, MLP, NN. and NN had the best results with an accuracy of 0.756
| No | Model | Accuracy |
|---|---|---|
| 1 | Support Vector Machine | 0.726158 |
| 2 | Multi-Layer Perceptron | 0.478202 |
| 3 | Neural Networks | 0.756131 |