Comparative Analysis of Healing Online Toxic Conversation Using Machine Learning Techniques

This paper explores the application of deep learning techniques to heal online conversations on social media platforms. Online comment sections are public spaces but are often toxic with hate speech, personal attacks and offensive language. This paper addresses the challenge of healthy communication in digital spaces. This review consists of comparative analysis of various deep learning models like BERT, GPT, SVM and RNN model– LSTM for natural language processing techniques to classify comments based on their toxicity levels. It is observed that BERT performs better among all with an accuracy of 92.4%. This helps to promote respectful online conversations. A reliable & dependable way to deal with comment toxicity