HATE SPEECH DETECTION USING DEEP LEARNING
Keywords:
CNN (Convolutional Neural Network), Hate speech and deep learning, emotion analysis, Social networking.Abstract
Hate speech detection has garnered significant attention from investigators in the fields between text and Natural Language Processing (NLP). analytics, resulting in a significant rise in related research. The purpose of this analysis is to provide a comprehensive resource that outlines the various advancements, strategies, The methods used in the fight against hate speech on Twitter. The goal 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 the period from 2015 and 2022. The initial investigation had been conducted Between December 2020 and was later revised in July 2022. 91 items in total that fulfilled predefined standards were selected for inclusion in this analysis. Despite the increasing volume of research, it is evident from the evaluation 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 on hate 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 posting hate messages, thereby lowering the possibility of specific harassment.
