Un-Compromised Credibility: Social Media based Multi-Class Hate Speech Classification for Text: A Review

Abstract

Abstract: Hate speech is a crime that has been on the rise in recent years, not just in face-to-face contacts but also online. This is due to a number of causes. On the one hand, due of the anonymity given by the internet and social networks in particular, people are more likely to engage in hostile behaviour. People's desire to voice their thoughts online, on the other side, have increased, adding to the spread of hate speech. Governments and social media platforms can benefit from detection and prevention techniques because this type of prejudiced speech can beimmensely destructive to society. We contribute to a solution to this dilemma by giving a systematic review of research undertakenin the subject through this survey. This challenge benefited from the use of several complicated and non-linear models, and CAT Boost performed best due to the application of latent semantic analysis (LSA) for dimensionality reduction. Keywords: Multi-Class Hate Speech, Natural Language Processing, Hate Speech Classification, Social Media Micro blogs, Multi-Class Hate SpeechDataset.