HATE SPEECH DETECTION USING DEEP LEARNING

Abstract— Hate speech detection has garnered significant attention from investigators in the fieldsbetween text and Natural Language Processing (NLP). analytics, resulting in a significant rise inrelated research. The purpose of this analysis is to provide a comprehensive resource that outlines thevarious advancements, strategies, The methods used in the fight against hate speech on Twitter. Thegoal is to offer valuable insights that can help researchers develop better models for next research.This evaluation focuses on research articles released within the previous eight years, covering theperiod from 2015 and 2022. The initial investigation had been conducted Between December 2020and was later revised in July 2022. 91 items in total that fulfilled predefined standards were selectedfor inclusion in this analysis. Despite the increasing volume of research, it is evident from theevaluation of these studies that a definitive solution to the problem has not yet been found.In conclusion, this project aims to provide a thorough understanding of the current perspectives onhate speech detection and identifies potential areas for future research to improve detection systems.The ultimate goal is to assist social media platforms in identifying and preventing users from postinghate messages, thereby lowering the possibility of specific harassment. Index Terms— CNN (Convolutional Neural Network), Hate speech and deep learning, emotionanalysis, Social networking.