Year: 2024
Type: article
Abstract: The massive expansion of social networking websites has made it easier for people with various cultural and psychological backgrounds to communicate directly with one another. It has led to an increase in online conflicts between them. This paper proposes an approach to detect hate speech. A publicly available dataset of tweets in language is used. Data preprocessing includes the removal of stopwords , punctuations , emojis , numbers , URLs etc. and feature extraction is carried out using tokenization , lemmatization and POS tagging. The performances of XGBoost, Random Forest , Logistic Regression and SVM have been compared in this study for the detection of hate speech. XGBoost classifier provided highest accuracy of 74.93%. Keywords: Hate speech, Hate tweets, Machine learning.
DOI: 10.55041/ijsrem34783
Author: Navneet Nikhil,
Institution: Don Bosco Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4399044228