BengaliHateCB: A Hybrid Deep Learning Model to Identify Bengali Hate Speech Detection from Online Platform

Online issues including hate speech, abusive communications, and harassment have been exacerbated by the rising number of Internet users. People in Bangladesh often face online harassment and threats expressed in Bengali on various social media platforms. Also, there has not been nearly enough investigation into the possibility of Offensive language in Bengali literature. Although finding realistic ways to reduce hate speech in Bengali texts is urgently needed, there is a notable lack of study in the area of Bengali abusive speech detection, despite the widespread detrimental impacts of abusive text on people's well-being. The results of this research provide a method for spotting bad hateful comments in Bengali online profiles. This research provides a methodology to identify potentially manipulative hate speech in Bengali social media postings. The BERT architecture is used to gather characteristics of Bengali texts. The next step in hate speech classification is to use a Convolutional Neural Network (CNN) model including a softMax activation function. We propose a new model, BERT-CNN, that combines both models. On the Bengali Hate Speech from Social Platforms (BD-SHS) dataset, the BERT-CNN model outperformed most baseline architectures, with accuracy, precision, recall, and F1-scores of 95.67%, 93.55%,92.67%, and 94.44%, respectively. According to our research, the method we suggested for spotting hate speech in Bengali writings posted on social networking sites works well, which can lessen online hate comments and foster a more civilized online community.