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.