IJIREM

Abstract

"Hate speech" refers to objectionable statements that may endanger societal harmony and targets a group or a person based on inborn qualities (such as race, religion, or gender). The issue of hate speech has been continuously growing on social media platforms lately. Our research focuses on creating a robust comment classifier that will categorize comments according to their toxicity. A series of activities or issues with getting a software to automatically categorize input comments into categories depending on the toxicity of the comment are referred to as comment classification. Our model combines LSTM and BERT with additional language processing methods. The application determines the category of toxic comments and shows the proportion of toxicity using an algorithmic technique.

Keywords

Artificial Intelligence, LSTM, BERT, Cyber Security, Machine Learning

Reference

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Cites this article as

[Anvita Karne, Vaishnavi Thakare, Sonal Fatangare (2023), Cyberbullying: Hate Comment Classifier, International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-10, Issue-1, Page No-123-125], (ISSN 2347 - 5552). www.ijirem.org

Corresponding Author

Anvita Karne

Student, Department of Computer Science & Engineering, Rasiklal M. Dhariwal School of Engineering, Pune, India

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