Hate Speech and Abusive Language and Abusive Language Detection in Twitter using Machine Learning

Abstract

Twitter's central goal is to enable everybody to make and share thoughts and data, and to communicate their suppositions and convictions without boundaries. Twitter's job is to serve the public discussion, which requires portrayal of a different scope of points of view. Yet, it does not advance viciousness against or straightforwardly assault or undermine others based on race, nationality, public cause, rank, sexual direction, age, inability, or genuine illness. Hate speech and abusive language can hurt a person or a community. So, it is not appropriate to use hate speech and abusive language. Now, due to increase in social media usage, hate speech and abusive language is very commonly used on these platforms. So, it is not possible to identify hate speech and abusive languagees manually. So, it is essential to develop an automated hate speech and abusive language detection model and this research work shows different approaches of Natural Language Processing for classification of Hate speech and abusive language through Machine Learning Algorithms.