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Enhancing the Performance of Hate Speech Classification Using Dimensionality Reduction Approach

Kaushar Ansari, Anshul Sarawagi


In recent times most of the people are using online platforms for sharing their emotions. These emotions can be classified as a positive comment or as a negative comment. But these comments play an important role when comments are made in the form of reviews for any particular purpose. Today all the ecommerce websites, election parties and many other online business forums are predicting these reviews for evaluating the performance of their product or work. Many times people used to post hate speech on social media, so it is very much need that we must predict the hate speech for further improvement. Traditional machine learning algorithms are not able to accurately predict the hate speech. In this work we have applied dimensionality reduction approach for performing the classification of hate speech on the basis of which classifiers has improved the performance. The feature selection approached is done through Information Gain, Term frequency–Inverse Document frequency and Logistic Regression Cross Validation and we have achieved the F1 score of 0.81, 0.90 and 0.87 for the gradient boosting, random forest, and extreme gradient boosting classifiers respectively.

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