A Performance Study of Applications of AI in Toxic Comments Classification

Abstract

Social media websites and tweeting apps have seen a sharp rise in popularity in the recent years. One can express their opinions and sentiments about things, people, and events through these platforms. Arguments frequently start on social media platforms during discussions and debates and involve the usage of toxic comments, which are unpleasant, disrespectful, and hurtful statements. According to many, social networking sites must be able to identify these harmful comments. This research analyses several deep learning and machine learning methods like Convolutional Neural Network, Long Short -Term Memory, Support Vector Machine, Random Forest, and Naive Bayes for toxic comments classification along with the study that examines the effects of many word embedding methods including Word2Vector, Bag of Words, Global Vectors, Bidirectional Encoder Representations from Transformers, and Embeddings from Language Model on the classification of toxic comments and also the future scope of the research.