A Machine Learning Approach to Comment Toxicity Classification

TL;DR

This paper presents a machine learning system that accurately detects various types of online comment toxicity, aiming to promote healthier online interactions.

Contribution

It introduces a 6-headed tf-idf based machine learning model for classifying multiple toxicity types with high accuracy, advancing automated moderation techniques.

Findings

Mean validation accuracy of 98.08%

Absolute validation accuracy of 91.61%

Effective detection of obscenity, threats, insults, and hatred

Abstract

Now-a-days, derogatory comments are often made by one another, not only in offline environment but also immensely in online environments like social networking websites and online communities. So, an Identification combined with Prevention System in all social networking websites and applications, including all the communities, existing in the digital world is a necessity. In such a system, the Identification Block should identify any negative online behaviour and should signal the Prevention Block to take action accordingly. This study aims to analyse any piece of text and detecting different types of toxicity like obscenity, threats, insults and identity-based hatred. The labelled Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A 6-headed Machine Learning tf-idf Model has been made and trained separately, yielding a Mean Validation Accuracy of 98.08% and Absolute Validation Accuracy of 91.61%. Such an Automated System should be deployed for enhancing healthy online conversation