UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 4
April-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2304B29


Registration ID:
514066

Page Number

l167-l172

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Title

DEEP LEARNING BASED HYBRID WORD REPRESENTATION FOR DETECTION OF HATE SPEECH

Abstract

Hate speech on social media platforms has become a major concern in recent years, with many users experiencing online harassment and abuse. In response, automated systems have been developed to detect and flag hate speech, but the effectiveness of these systems is still a subject of debate. In this paper, we present a hate speech detection system for a Twitter-like interface. Our system uses natural language processing techniques to analyze user posts and identify hate speech, and blocks users who repeatedly violate our hate speech policy. We evaluate the performance of our system using a dataset of tweets labelled for hate speech, and compare our results to existing hate speech detection systems. Our findings suggest that our system performs well in identifying hate speech and blocking repeat offenders, but also highlight the challenges of developing fair and unbiased automated systems. We conclude by discussing the implications of our findings for the use of automated hate speech detection on social media platforms, and suggest areas for further research.

Key Words

Keywords: Deep Learning, Hybrid Word Representation, Hate Speech Detection, Distributed Representation, Symbolic Representation, Neural Network, Linguistic Properties, Part-Of-Speech Tags, Dependency Relationships, Benchmark Dataset, Generalization

Cite This Article

"DEEP LEARNING BASED HYBRID WORD REPRESENTATION FOR DETECTION OF HATE SPEECH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.l167-l172, April-2023, Available :http://www.jetir.org/papers/JETIR2304B29.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"DEEP LEARNING BASED HYBRID WORD REPRESENTATION FOR DETECTION OF HATE SPEECH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppl167-l172, April-2023, Available at : http://www.jetir.org/papers/JETIR2304B29.pdf

Publication Details

Published Paper ID: JETIR2304B29
Registration ID: 514066
Published In: Volume 10 | Issue 4 | Year April-2023
DOI (Digital Object Identifier):
Page No: l167-l172
Country: Kopargaon, maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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