@inproceedings{v-etal-2025-code,
title = "{C}ode{\_}{C}onquerors@{D}ravidian{L}ang{T}ech 2025: Deep Learning Approach for Sentiment Analysis in {T}amil and {T}ulu",
author = "V, Harish Vijay and
Srichandra, Ippatapu Venkata and
Rao, Pathange Omkareshwara and
B, Premjith",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.44/",
doi = "10.18653/v1/2025.dravidianlangtech-1.44",
pages = "254--258",
ISBN = "979-8-89176-228-2",
abstract = "In this paper we propose a novel approach to sentiment analysis in languages with mixed Dravidian codes, specifically Tamil-English and Tulu-English social media text. We introduce an innovative hybrid deep learning architecture that uniquely combines convolutional and recurrent neural networks to effectively capture both local patterns and long-term dependencies in code-mixed text. Our model addresses critical challenges in low-resource language processing through a comprehensive preprocessing pipeline and specialized handling of class imbalance and out-of-vocabulary words. Evaluated on a substantial dataset of social media comments, our approach achieved competitive macro F1 scores of 0.3357 for Tamil (ranked 18) and 0.3628 for Tulu (ranked 13)"
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%0 Conference Proceedings
%T Code_Conquerors@DravidianLangTech 2025: Deep Learning Approach for Sentiment Analysis in Tamil and Tulu
%A V, Harish Vijay
%A Srichandra, Ippatapu Venkata
%A Rao, Pathange Omkareshwara
%A B, Premjith
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F v-etal-2025-code
%X In this paper we propose a novel approach to sentiment analysis in languages with mixed Dravidian codes, specifically Tamil-English and Tulu-English social media text. We introduce an innovative hybrid deep learning architecture that uniquely combines convolutional and recurrent neural networks to effectively capture both local patterns and long-term dependencies in code-mixed text. Our model addresses critical challenges in low-resource language processing through a comprehensive preprocessing pipeline and specialized handling of class imbalance and out-of-vocabulary words. Evaluated on a substantial dataset of social media comments, our approach achieved competitive macro F1 scores of 0.3357 for Tamil (ranked 18) and 0.3628 for Tulu (ranked 13)
%R 10.18653/v1/2025.dravidianlangtech-1.44
%U https://aclanthology.org/2025.dravidianlangtech-1.44/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.44
%P 254-258
Markdown (Informal)
[Code_Conquerors@DravidianLangTech 2025: Deep Learning Approach for Sentiment Analysis in Tamil and Tulu](https://aclanthology.org/2025.dravidianlangtech-1.44/) (V et al., DravidianLangTech 2025)
ACL
- Harish Vijay V, Ippatapu Venkata Srichandra, Pathange Omkareshwara Rao, and Premjith B. 2025. Code_Conquerors@DravidianLangTech 2025: Deep Learning Approach for Sentiment Analysis in Tamil and Tulu. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 254–258, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.