Detecting Hate Speech In X(Twitter) Using Sentiment Analysis

Siddhant Roy , Girikratna Sharma

Hate speech, sentiment analysis, twitter, machine learning

Accurately separating hate speech from offensive language is difficult for computerized hate-speech detection on social media. Currently used techniques frequently lack accuracy, and supervised learning is ineffective at successfully differentiating between these categories. This study collects tweets with hate speech keywords and, using a crowd-sourced hate speech lexicon, labels them as either hate speech, offensive language, or neither. A multi-class classifier is trained to distinguish between these categories, showing instances where it is more challenging to distinguish between hate speech and offensive language. The study discovered its classification of tweets as hate speech was less probable for those containing homophobic or racist rhetoric in contrast to those espousing sexist or racist perspectives, which were more likely to receive such designation. Additionally, tweets without clear hateful language provide a bigger classification challenge.

Published Paper   E-Certificate

"Detecting Hate Speech In X(Twitter) Using Sentiment Analysis", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 9, page no.581 - 585, September-2023, Available :https://ijsdr.org/papers/IJSDR2309085.pdf

Volume 8 Issue 9, September-2023

Pages : 581 - 585

Paper Reg. ID: IJSDR_208585

Published Paper Id: IJSDR2309085

Downloads: 000347227

Research Area: Computer Science & Technology 

Country: Mumbai, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2309085

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2309085

DOI: Cite as Siddhant Roy, & Girikratna Sharma. (2023). Detecting Hate Speech In X(Twitter) Using Sentiment Analysis. International Journal of Scientific Development and Research, 8(9), 581–585. https://doi.org/10.5281/zenodo.8373613

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition