The increasing expansion of Internet users has resulted in unwanted cyber concerns such as cyberbullying, hate speech, and a slew of others. This paper deals with the reviewing of different techniques used to detect hate speech by many scholars and researchers. Hate speech occurs when an individual or a group of individuals attack or use derogatory or discriminatory words towards a group of people based on characteristics such as origin, sexuality, ethnicity, religious background, socioeconomic status, race, gender, and other factors. When such action takes place on social networking sites, blogs, creative material, and other forms of online media, it is referred to as Online Hate speech [1]. Hate speech appears to be an explosive kind of communication that uses misunderstandings to promote a hate ideology. Hate speech targets a variety of protected characteristics, such as gender, religion, color, and disability.[2]. Hence it becomes to monitor every post and try to filter out hate speech spreading posts. Concerning this aspect, many techniques have been published using different aspects of machine learning and deep learning. Several attempts to categorize hate speech using machine learning have been performed, with this one focusing on the use of rudimentary NLP feature engineering approaches
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