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ktrain

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Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2…

  • Updated Sep 26, 2022
  • Python

Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.

  • Updated Nov 3, 2020
  • Jupyter Notebook

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. So, in this application, we are asking a YouTuber to enter the channel id and a particular timeline. By using the channel id a…

  • Updated Dec 28, 2022
  • SCSS

This project explores advanced NLP techniques using RoBERTa and XLNet models to classify human emotions from textual data. By leveraging robust training and optimization methods, it aims to enhance emotion detection accuracy in applications like social media analysis and customer support.

  • Updated Jul 24, 2024
  • Jupyter Notebook

Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies). But, it’s not always cle…

  • Updated Sep 26, 2021
  • Jupyter Notebook

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