Multi Label Toxic Comment Classification u sing Machine Learning Algorithms
Abhishek Aggarwal1, Atul Tiwari2
1Abhishek Aggarwal, Bachelor of Technology in Electrical Engineering, Delhi Technological University, Delhi, India.
2Atul Tiwari*, Bachelor of Technology in Electrical Engineering, Delhi Technological University, Delhi, India.
Manuscript received on May 07, 2021. | Revised Manuscript received on May 15, 2021. | Manuscript published on May 30, 2021. | PP: 185-161 | Volume-10 Issue-1, May 2021. | Retrieval Number: 100.1/ijrte.A58140510121 | DOI: 10.35940/ijrte.A5814.0510121
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Accuracy, Multilabel Classification, Machine Learning Algorithms, Toxic Comments.