Skip to content

Project Ignite in mentorship of the American Express R&D team

Notifications You must be signed in to change notification settings

ZL-Rong/Hate-Speech-Detection-BERT

 
 

Repository files navigation

Hate Speech Detection

  • American Express Ignite Project 2019
  • Pranav D. Pawar ; Mentor : Lokesh Kumar Kriplani

Detailed documentation and experiments details - here

Flask Web App

  • Primary features of API -

    • Custom Text Input testing - Given a text input, we can generate the probability of hate speech with an F1-Score of 94% (using BERT model)
    • Hashtag analysis -
      • Given a valid hashtag, API scrapes the latest n tweets for that hashtag and performs an evaluation on it using our deployed model.
      • Finally generates a sorted list of tweets according to their hate probability.
      • Here the input is a hashtag, no. of tweets, and date since you want to perform the evaluation upon
    • User analysis
      • Given a valid twitter user ID, API scrapes the latest n tweets on the user’s timeline and similar to the previous case generates a table of a sorted list of tweets according to their hate probability.
      • Here the input is only the hashtag and no. of tweets to scrape
  • BERT App Service

  • XGBoost App Service

References -

  1. Deep Learning for Hate Speech Detection in Tweets
  2. Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter
  3. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

About

Project Ignite in mentorship of the American Express R&D team

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 98.2%
  • Other 1.8%