@inproceedings{tash-etal-2023-lidoma,
title = "{LIDOMA}@{D}ravidian{L}ang{T}ech: Convolutional Neural Networks for Studying Correlation Between Lexical Features and Sentiment Polarity in {T}amil and {T}ulu Languages",
author = "Tash, Moein and
Armenta-Segura, Jesus and
Ahani, Zahra and
Kolesnikova, Olga and
Sidorov, Grigori and
Gelbukh, Alexander",
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-1.25",
pages = "180--185",
abstract = "With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively",
}
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<abstract>With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively</abstract>
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%0 Conference Proceedings
%T LIDOMA@DravidianLangTech: Convolutional Neural Networks for Studying Correlation Between Lexical Features and Sentiment Polarity in Tamil and Tulu Languages
%A Tash, Moein
%A Armenta-Segura, Jesus
%A Ahani, Zahra
%A Kolesnikova, Olga
%A Sidorov, Grigori
%A Gelbukh, Alexander
%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 tash-etal-2023-lidoma
%X With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively
%U https://aclanthology.org/2023.dravidianlangtech-1.25
%P 180-185
Markdown (Informal)
[LIDOMA@DravidianLangTech: Convolutional Neural Networks for Studying Correlation Between Lexical Features and Sentiment Polarity in Tamil and Tulu Languages](https://aclanthology.org/2023.dravidianlangtech-1.25) (Tash et al., DravidianLangTech-WS 2023)
ACL
- Moein Tash, Jesus Armenta-Segura, Zahra Ahani, Olga Kolesnikova, Grigori Sidorov, and Alexander Gelbukh. 2023. LIDOMA@DravidianLangTech: Convolutional Neural Networks for Studying Correlation Between Lexical Features and Sentiment Polarity in Tamil and Tulu Languages. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 180–185, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.