@inproceedings{ponnusamy-etal-2023-vel,
title = "{VEL}@{D}ravidian{L}ang{T}ech: Sentiment Analysis of {T}amil and {T}ulu",
author = "Ponnusamy, Kishore Kumar and
Rajkumar, Charmathi and
Kumaresan, Prasanna Kumar and
Sherly, Elizabeth and
Priyadharshini, Ruba",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.30",
pages = "211--216",
abstract = "We participated in the Sentiment Analysis in Tamil and Tulu - DravidianLangTech 2023-RANLP 2023 task in the team name of VEL. This research focuses on addressing the challenge of detecting sentiment analysis in social media code-mixed comments written in Tamil and Tulu languages. Code-mixed text in social media often deviates from strict grammar rules and incorporates non-native scripts, making sentiment identification a complex task. To tackle this issue, we employ pre-processing techniques to remove unnecessary content and develop a model specifically designed for sentiment analysis detection. Additionally, we explore the effectiveness of traditional machine-learning models combined with feature extraction techniques. Our best model logistic regression configurations achieve impressive macro F1 scores of 0.43 on the Tamil test set and 0.51 on the Tulu test set, indicating promising results in accurately detecting instances of sentiment in code-mixed comments.",
}
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<abstract>We participated in the Sentiment Analysis in Tamil and Tulu - DravidianLangTech 2023-RANLP 2023 task in the team name of VEL. This research focuses on addressing the challenge of detecting sentiment analysis in social media code-mixed comments written in Tamil and Tulu languages. Code-mixed text in social media often deviates from strict grammar rules and incorporates non-native scripts, making sentiment identification a complex task. To tackle this issue, we employ pre-processing techniques to remove unnecessary content and develop a model specifically designed for sentiment analysis detection. Additionally, we explore the effectiveness of traditional machine-learning models combined with feature extraction techniques. Our best model logistic regression configurations achieve impressive macro F1 scores of 0.43 on the Tamil test set and 0.51 on the Tulu test set, indicating promising results in accurately detecting instances of sentiment in code-mixed comments.</abstract>
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%0 Conference Proceedings
%T VEL@DravidianLangTech: Sentiment Analysis of Tamil and Tulu
%A Ponnusamy, Kishore Kumar
%A Rajkumar, Charmathi
%A Kumaresan, Prasanna Kumar
%A Sherly, Elizabeth
%A Priyadharshini, Ruba
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F ponnusamy-etal-2023-vel
%X We participated in the Sentiment Analysis in Tamil and Tulu - DravidianLangTech 2023-RANLP 2023 task in the team name of VEL. This research focuses on addressing the challenge of detecting sentiment analysis in social media code-mixed comments written in Tamil and Tulu languages. Code-mixed text in social media often deviates from strict grammar rules and incorporates non-native scripts, making sentiment identification a complex task. To tackle this issue, we employ pre-processing techniques to remove unnecessary content and develop a model specifically designed for sentiment analysis detection. Additionally, we explore the effectiveness of traditional machine-learning models combined with feature extraction techniques. Our best model logistic regression configurations achieve impressive macro F1 scores of 0.43 on the Tamil test set and 0.51 on the Tulu test set, indicating promising results in accurately detecting instances of sentiment in code-mixed comments.
%U https://aclanthology.org/2023.dravidianlangtech-1.30
%P 211-216
Markdown (Informal)
[VEL@DravidianLangTech: Sentiment Analysis of Tamil and Tulu](https://aclanthology.org/2023.dravidianlangtech-1.30) (Ponnusamy et al., DravidianLangTech-WS 2023)
ACL
- Kishore Kumar Ponnusamy, Charmathi Rajkumar, Prasanna Kumar Kumaresan, Elizabeth Sherly, and Ruba Priyadharshini. 2023. VEL@DravidianLangTech: Sentiment Analysis of Tamil and Tulu. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 211–216, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.