Classifying and measuring hate speech in Twitter using topic classifier of sentiment analysis

Abstract

The aim and objective of this research are to create a model to measure the hate speech and to measure the contents of hate speech. The descriptive analysis method of data science was used to describe and summarize raw data from a dataset. We used Twitter as the social networking Web site for this research to analyze and measure the hate speech and its classifications. A dataset from kaggle datasets was applied for this research. To produce statistical results, we used monkey learn machine learning libraries which are incorporated with Python program to design and develop a model to classify and measure hate speech and its types that could be trained and tested using sentiment analysis. Researchers have found that the majority of the tweets are based on racist and ethnicity, sex and religion-based hate speech are also widely available.