Classification of Toxic Comments on Social Networks Using Machine Learning

This research addresses the problem of toxic comments in social networks, and how artificial intelligence (AI) and machine learning (Machine Learning) can help. It presents the development of a classification model using AI with machine learning techniques to identify toxic comments on Twitter. The proposed classifier, developed in Python, was established with 7 different algorithms using approaches or strategies for multi-label classification, preprocessing, cleaning and data visualization. This model was trained with a total of 159571 comments from the Kaggle repository dataset called Jigsaw, which has the comments classified with various features. After the training, evaluation and comparison of the model created, the result was a classifier capable of identifying toxic and offensive words or comments with an accuracy of 92.16%.