A Comparative Analysis of Identifying Multi Class Toxic Comment Classification Utilizing Deep Learning and Machine Learning Technologies

Abstract

Despite extensive efforts to prevent and manage malicious human behavior, there are still many compelling issues in cyber platforms. When such individuals live in a world where technology is rapidly developing and where various online social media platforms like Twitter, Facebook, Instagram, etc. are widely accessible, those nefarious human acts rise. Because of this, extremely dangerous crimes like toxic comments, which pose a threat to users, groups, and even governments, have increased in frequency. This study aims to identify such harmful comments in Bangla text that are posted on social media sites, classify them using machine learning and deep learning algorithms, and take preventative measures. By combining TF-IDF with various machine learning algorithms, such as Naive Bayes, Decision Tree, Random Forest, AdaBoost Classifier, Stochastic Gradient Descent Classifier, Logistic Regression, KNN, and Support Vector Machine, we were able to detect toxic comments with an accuracy of 76.5%, 78.2%, 75.0%, 75.1%, 76.2%, 77.7%, 70.2%, and 78.5%, respectively. In comparison to previous machine learning techniques, we achieved an accuracy of 89% by employing deep learning algorithms