A Review on Machine Learning Based Approaches for Identifying Hate Speech

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Rashika Chouhan; Dr. Ati Jain

A Review on Machine Learning Based Approaches for Identifying Hate Speech

Authors: Rashika Chouhan, Dr. Ati Jain

Unique Paper ID: 172452
Volume: 11
Issue: 8
PageNo: 3214-3218

Keywords: Natural Language Processing (NLP) Hate Speech Social Media Contextual Dependency Classification Accuracy.

Abstract:
Hate speech, abusive words, threat, derogation are some examples of such incidents. Abuse in the form of hate speech is not only applicable to one gender, it is applicable to everyone. In the current scenario understanding the dynamics patterns (incidents, geographical prevalence, demographics, etc.) is crucial in designing strategies to analyze the hate speech activities. Social media platforms are acting as an information-based system that collects and organizes hate speech related information from various sources (namely users). This collected information is analyzed to extract knowledgeable patterns from huge amount of social media data which is not possible to monitor in every minute. Contextual dependency among various lexicons in data will be necessary to detect hate speech. Conventional NLP models such as CNNs, BERT focus on features as tokens of repetitive annotated hate speech cases. This paper presents a comprehensive review of the existing approaches in the domain of research highlighting the obtained results of the approaches.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 8
PageNo: 3214-3218

A Review on Machine Learning Based Approaches for Identifying Hate Speech

Available:https://ijirt.org/article?manuscript=172452

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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