A Supervised Classification Approach for Detecting Hate Speech in English Tweets

Abstract

As social concerns about threats of hatred and harassment have grown on the internet, there has been a lot of attention paid to detecting hate speech. This research looks at how well SGD classifiers with hyper-parameter tuning perform at detecting hate speech in tweets. It describes the categorization of English tweets with stochastic gradient descent (SGD) classifiers. The categorization of text documents depends on their content, which is divided into groups based on predefined categories. The Term-Frequency (TF) and Inverse-Document Frequency (IDF) parameters are implemented in the proposed system. A Stochastic Gradient Descent method (SGD) is used to generate classifiers that learn independent features, and performance is assessed using Accuracy and F1-score.