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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.
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.
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.
Identifying trolling, aggression, cyber-bullying and hate speech etc. Three classes: Overtly Aggressive (OAG), Covertly Aggressive (CAG), and Non-aggressive (NAG)