Cyberbullying Tweet Analysis Using NLP and Machine Learning Techniques

Twitter cyberbullying has grown to be a pervasive and harmful problem that has significant adverse effects on both the psychological wellness of people and the health of online communities. With an emphasis on Natural Language Processing (NLP) and machine learning techniques, this study explores the efficient detection and analysis of cyberbullying using data from Twitter. In order to figure out the subtleties of damaging communication, we investigate methods like sentiment analysis, contextual embedding, attention-based deep learning, and supervised categorization. The study also looks at the social aspects of cyberbullying, the limits of current models, and moral concerns with data collection. The results show that while detection algorithms are improving substantially there are still issues, especially with sarcasm comprehension, changing slang, and linguistic context. Recommendations for real-time interventions, policy improvements, and the contribution of AI to accelerating the creation of safer social media platforms are included in the paper’s conclusion.