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A Hybrid Collaborative Clustering Approach for Noise-Robust Speech Recognition

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A Hybrid MuRIL–Attention–Random Forest Framework for Hate Speech Detection Against Women in Hindi

by Neha Tyagi, Gopal Krishna Sharma, Narendra Kumar Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 96
Year of Publication: 2026
Authors: Neha Tyagi, Gopal Krishna Sharma, Narendra Kumar Sharma
10.5120/ijca2ad422afaaf6

Neha Tyagi, Gopal Krishna Sharma, Narendra Kumar Sharma . A Hybrid MuRIL–Attention–Random Forest Framework for Hate Speech Detection Against Women in Hindi. International Journal of Computer Applications. 187, 96 ( Apr 2026), 51-59. DOI=10.5120/ijca2ad422afaaf6

@article{ 10.5120/ijca2ad422afaaf6,
author = { Neha Tyagi, Gopal Krishna Sharma, Narendra Kumar Sharma },
title = { A Hybrid MuRIL–Attention–Random Forest Framework for Hate Speech Detection Against Women in Hindi },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2026 },
volume = { 187 },
number = { 96 },
month = { Apr },
year = { 2026 },
issn = { 0975-8887 },
pages = { 51-59 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number96/a-hybrid-murilattentionrandom-forest-framework-for-hate-speech-detection-against-women-in-hindi/ },
doi = { 10.5120/ijca2ad422afaaf6 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-04-19T00:40:22.854586+05:30
%A Neha Tyagi
%A Gopal Krishna Sharma
%A Narendra Kumar Sharma
%T A Hybrid MuRIL–Attention–Random Forest Framework for Hate Speech Detection Against Women in Hindi
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 96
%P 51-59
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Societal hate speech against women on social media is growing, especially in dialects with limited resources like Hindi, where diversity of linguistics, unofficial writing styles, and social stratification make machine-generated detection fail. Modern ML and learning methods struggle to capture contextual semantics and control differences, resulting in low accuracy. This study proposes a blended framework that combines MuRIL, a multilingual transformer-inspired language model for Indian languages, with a focus mechanism and a random forest classifier to recognize Hindi sexist comments directed at women. MuRIL embeds provide deep background visualizations, while the attention layer reveals patterns of hateful language. The 2,020 professionally labelled Hindi social network database is used for comprehensive assessments. The proposed hybrid framework is compared against TF-IDF with SVM, CNN, Bi-LSTM with attention, and separate MuRIL-based models. Studies show that the MuRIL–Attention–Random Forest design outperforms traditional models in targeted detection, with an average precision of 92.82% and a greater group-wise difference. Using transformer-driven meaning representations with machine learning ensembles improves spotting accuracy in limited-resource and unbalanced situations. configurations. The current arrangement is an effective and durable solution for Hindi offensive language identification and a solid foundation for multilingual and continuing regulatory system.

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Index Terms

Computer Science
Information Sciences

Keywords

Hate speech Hybrid framework women machine and deep learning models

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