Hate Speech Detection in Twitter Using Different Models

Abstract

Twitter’s primary objective is to facilitate free expression and the exchange of ideas, allowing individuals to share their thoughts, opinions, and information with others without any limitations or constraints. It helps a human being to perceive different scopes and points of view. It is used to serve the public discussion and it should not be used to undermine individuals based on their race, nationality, public standing, rank, sexual orientation, age, disability, or health conditions. So, using hate speech is not appropriate and removal of hate speech is necessary for achieving the goal. This paper aims to utilize machine learning algorithms such as Logistic Regression, Support Vector Machine, Random Forest, CNN-LSTM, and Fuzzy method to compare and evaluate their accuracy in detecting hate speech. The objective is to determine the best model for hate speech detection.