Open AccessMulti Label Toxic Comment Classification using Machine Learning Algorithms
Author(s) -
Ankur Aggarwal,
Atul Tiwari
Publication year - 2021
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a5814.0510121
Subject(s) - viewpoints , offensive , conversation , limiting , computer science , categorization , scope (computer science) , machine learning , the internet , artificial intelligence , internet privacy , algorithm , computer security , world wide web , psychology , operations research , engineering , communication , mechanical engineering , art , visual arts , programming language
Toxic comments are the comments found in the online forums that are rude, offensive, or unfair and usually cause many users to exit the conversation. The threat of bullying and abuse on the internet obstructs the free exchange of ideas by limiting people’s opposing viewpoints. Most of the Websites fail to successfully facilitate healthy conversations, leading them to either restrict or disable user comments entirely. This paper would explore the scope of online abuse and categorize them into different labels to assess the toxicity as accurately as possible using machine learning algorithms.