International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Performance Analysis of Logistic Regression, Naive Bayes, KNN, Decision Tree, Random Forest and SVM on Hate Speech Detection from Twitter

Abstract

Hate speech specially racism, gender and religion discrimination, defaming comments are becoming one of the biggest problems in Twitter these days, that are making people to switch to other social media. Its effect is long-standing and unpreventable. To stop hateful activities from happening, Machine Learning approaches are needed to be applied. This research article focuses on the performance analysis and effectiveness of Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest and Support Vector Machine on detection of hate speech from Twitter. SVM, Decision Tree and Random Forest outperformed all the other models, achieving state-of-art 95.5%, 96.2% and 98.2% accuracy respectively on comments gather over a stretch.

Country : India

1 Subhajeet Das2 Koushikk Bhattacharyya3 Sonali Sarkar

  1. Department of Computer Science & Engineering, Swami Vivekananda Institute of Science & Technology, Kolkata, India
  2. Department of Computer Science & Engineering, Swami Vivekananda Institute of Science & Technology, Kolkata, India
  3. Department of Chemical Engineering, Swami Vivekananda Institute of Science & Technology, Kolkata, India

IRJIET, Volume 7, Issue 3, March 2023 pp. 24-28

doi.org/10.47001/IRJIET/2023.703004

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