Bengali Cyberbullying Text Detection: A Comprehensive Study

Cyberbullying is a serious online problem that involves harassment and harm via digital communication systems. It can have serious effects on people’s mental health and well-being. This research aims to address cyberbullying in Bengali, a low-resource language that has gotten little attention. Previous machine learning approaches for Bengali cyberbullying detection relied on feature extraction techniques that failed to accurately capture word meanings. To address this restriction, this study suggests using word embedding, which portrays words as multidimensional vectors that capture their semantic interactions. The research will compare several machine learning algorithms and feature extraction techniques in order to give a thorough analysis of their accuracy and performance measures. This study intends to contribute to the development of a sophisticated cyberbullying detection model for Bengali. The results demonstrate that one of the ensemble-based models outperforms individual models, achieving a higher accuracy of 0.796, recall of 0.796, and precision of 0.804. The findings of this study promise to make a substantial contribution against cyberbullying in Bengali. This study aims to establish the framework for future breakthroughs in the field of linguistic cybersecurity through its insightful results and methodological approach.