Toxic Comment Analysis for Online Learning

Abstract

Due to recent circumstances of the pandemic, online platforms are becoming more and more essential for communication in many sectors. But because of this, a lot of negativity and toxic comments are surfacing, resulting in degradation and online abuse. Educational systems and Institutions heavily rely on such platforms for e-learning leading to unrestricted attacks of toxic and negative comments towards teachers and students. Due to this work, issues of constant bullying and online abuse will be reduced. The comments classified are according to the parameters from our self-prepared dataset combined with Kaggle's toxic comment dataset, named as toxic, severely toxic, obscene, threat, insult, and identity hate. Machine Learning algorithms such as Logistic Regression, Random Forest, and Multinomial Naive Bayes are used. For data evaluation, ROC and Hamming scores are used. The output will be shown as the rate of each category in percentile and in a graphical format. This work will help reduce the online bullying and harassment faced by teachers and students and help create a non-toxic learning environment. In this way, the main focus will be on studying and not getting de-motivated and discouraged by hateful comments and people commenting toxic comments will also get reduced.