Cyberbullying Detection System Using Natural Language Processing and Machine Learning

Cyberbullying has become a serious issue on social media and online platforms, negatively impacting mental health and digital safety. This project proposes a Cyberbullying Detection System that leverages Natural Language Processing (NLP) and Machine Learning (ML) techniques to automatically identify offensive, abusive, or harmful text. By integrating advanced deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer-based architectures like BERT, the system can effectively capture context, sarcasm, and hidden abusive patterns. The approach involves analyzing user-generated content, extracting linguistic and semantic features, and classifying whether the text constitutes bullying or non-bullying. With accurate detection and contextual understanding, this system helps reduce online harassment, supports early intervention, and promotes a safer digital environment.