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Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.
This project focuses on the analysis of cyberbullying tweets categorized by various cyberbullying types. Using traditional Machine Learning Models, it aims to predict cyberbullying types in new tweets and provides insightful visualizations through Streamlit.
In this paper, we have implemented SVM, Bayesian and CNN, LSTM Neural network models for cyber bullying detection using Azure ML studio and compared their results.
This repository is comprised of files that contributed, one way or another, to the creation of the project entitled, "Quickgarde: A Plug-in for Detecting Cyberbullying Occurrences in Filipino Social Media Posts
Identifying trolling, aggression, cyber-bullying and hate speech etc. Three classes: Overtly Aggressive (OAG), Covertly Aggressive (CAG), and Non-aggressive (NAG)
Cyberbullying Detection App tailored for the Arabic language. The app is designed to identify instances of cyberbullying in Arabic text using various machine learning and deep learning algorithms.
This application can detect the hate and toxic tweets of users amongst an entire corpora of tweets and thus can be an effective methodology towards reducing cyber bullying.