Abstract

A multi-model supervised technique is employed to predict incidences of cyberbullying on Twitter in a thorough manner. The suggested approach makes use of sentiment analysis, natural language processing, and machine learning techniques to detect and prevent cyberbullying activities. The technology helps to create a safer online environment by precisely classifying potentially hazardous information by analyzing Twitter data. Combining several models improves the robustness and accuracy of predictions. The research uses cutting-edge analytical methods to identify harmful interactions and promote pleasant online interactions, which helps to avoid cyberbullying proactively.