Toxic Comments Classification using Neural Network
Rinal Patel1, Hetal Gaudani2

1Rinal Patel, Department of Computer Engineering, G. H. Patel College Of Engineering, V V Nagar (Gujarat), India.

2Hetal Gaudani, Professor, Department of Computer Engineering, Gujarat Technological University, (Gujarat), India.

Manuscript received on 25 April 2020 | Revised Manuscript received on 07 May 2020 | Manuscript Published on 22 May 2020 | PP: 12-15 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10050597S20 | DOI: 10.35940/ijitee.G1005.0597S20

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Abstract: Humans have built broad models of expressing their thoughts via several appliances. The internet has not only become a credible method for expressing one’s thoughts, but is also rapidly becoming the single largest means of doing so. In this context, one area of focus is the study of negative online behaviors of users like, toxic comments that are threat, obscenity, insults and abuse. The task of identifying and removing toxic communication from public forums is critical. The undertaking of analyzing a large corpus of comments is infeasible for human moderators. Our approach is to use Natural Language Processing (NLP) techniques to provide an efficient and accurate tool to detect online toxicity. We apply TF-IDF feature extraction technique, Neural Network models to tackle a toxic comment classification problem with a labeled dataset from Wikipedia Talk Page.

Keywords: Natural Language Processing, Neural Network, TF-IDF Feature Extraction, Toxic Comments.
Scope of the Article: Classification