A hybrid deep learning model and explainable AI-based Bengali hate speech multi-label classification and interpretation

Abstract

Data innovation has moved quickly in recent years, and various unfavorable alter ations have been made to the network medium. Social media platforms like Face book, Twitter, and Instagram are becoming more and more popular because they allow users to express their opinions through messages, photographs, and notes. In particular, in Bangladesh and other locations where the Bengali language is spoken. In any case, it has regrettably turned into a space with toxic remarks, cyberbully ing, and unidentified hazards. Numerous studies have been conducted in this area, but none have produced accurate results. Some effective pre-trained transformer models have been introduced. To identify Bengali malicious and non-malicious text at an early stage using simple Natural Language Processing (NLP). This study sug gests a Convolutional Neural Network with Bi-Directional Long Short-Term Memory (CNN-BiLSTM) hybrid strategy. This model can also classify any Bengali text data into six levels. Additionally, the transformed dataset is subjected to several conven tional Machine Learning methods using an estimator, and Explainable AI interprets these techniques (XAI). In the last stage, Stacking Classifier which is superior to any prior activity is used to ensemble all classifiers and the estimator.