Detecting Hate Speech and Offensive Language on Twitter using Machine Learning: An N-gram and TFIDF based Approach
TABLE V: Confusion Matrix for the evaluated test data on the final Logistic Regression model
['Class', 'Classified as Hateful', 'Classified as Offensive', 'Classified as Clean']
The key point to notice here is that 4.8% of the tweets that are offensive have been classified as hateful. Improvements can be done in this area to further increase the scores of the model. The final testing accuracy of the model is obtained to be 95.6%.