A Novel LSTM-Based Model for Sentiment Detection in Hindi- English Code-Switched Texts

Namitha Bhat, Kuldeep Sambrekar, Shridhar Allagi

2025

Abstract

Sentiment analysis in multilingual conversations is laborious yet important in understanding emotions and opinions expressed across multiple languages and cultures. Code-switching, a prevalent technique poses challenges due to linguistic diversity, cultural nuances, and contextual dependencies. The research in this article provides an LSTM-based framework for sentiment analysis in Hindi-English code-switched text, addressing the challenges of multilingual content in social media. The methodology adopted in this research incorporates three key components: language-specific encoders to obtain linguistic patterns, a switcher module for understanding language transitions, and a sentiment analysis module for extracting sentiment within a multilingual text. A Hindi-English dataset containing 4,954 samples with positive, neutral, and negative sentiments is used for training and evaluation. The model achieves an overall accuracy of 89.9%, and an F1-score of 0.9 across all sentiments investigated. This work contributes substantially to multilingual sentiment analysis, eliminating the shortcomings of conventional approaches and offering a robust method for analyzing complex code-switched text.

Paper Citation

in Harvard Style

Bhat N., Sambrekar K. and Allagi S. (2025). A Novel LSTM-Based Model for Sentiment Detection in Hindi- English Code-Switched Texts. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 422-428. DOI: 10.5220/0013593600004664

in Bibtex Style

@conference{incoft25,
author={Namitha Bhat and Kuldeep Sambrekar and Shridhar Allagi},
title={A Novel LSTM-Based Model for Sentiment Detection in Hindi- English Code-Switched Texts},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={422-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013593600004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - A Novel LSTM-Based Model for Sentiment Detection in Hindi- English Code-Switched Texts
SN - 978-989-758-763-4
AU - Bhat N.
AU - Sambrekar K.
AU - Allagi S.
PY - 2025
SP - 422
EP - 428
DO - 10.5220/0013593600004664
PB - SciTePress