@inproceedings{tripty-etal-2024-cuetsentimentsillies,
title = "{CUETS}entiment{S}illies@{D}ravidian{L}ang{T}ech-{EACL}2024: Transformer-based Approach for Sentiment Analysis in {T}amil and {T}ulu Code-Mixed Texts",
author = "Tripty, Zannatul and
Nafis, Md. and
Chowdhury, Antu and
Hossain, Jawad and
Ahsan, Shawly and
Das, Avishek and
Hoque, Mohammed Moshiul",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.39",
pages = "234--239",
abstract = "Sentiment analysis (SA) on social media reviews has become a challenging research agenda in recent years due to the exponential growth of textual content. Although several effective solutions are available for SA in high-resourced languages, it is considered a critical problem for low-resourced languages. This work introduces an automatic system for analyzing sentiment in Tamil and Tulu code-mixed languages. Several ML (DT, RF, MNB), DL (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLM-RoBERTa, m-BERT) are investigated for SA tasks using Tamil and Tulu code-mixed textual data. Experimental outcomes reveal that the transformer-based models XLM-R and m-BERT surpassed others in performance for Tamil and Tulu, respectively. The proposed XLM-R and m-BERT models attained macro F1-scores of 0.258 (Tamil) and 0.468 (Tulu) on test datasets, securing the $2^{nd}$ and $5^{th}$ positions, respectively, in the shared task.",
}
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%0 Conference Proceedings
%T CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based Approach for Sentiment Analysis in Tamil and Tulu Code-Mixed Texts
%A Tripty, Zannatul
%A Nafis, Md.
%A Chowdhury, Antu
%A Hossain, Jawad
%A Ahsan, Shawly
%A Das, Avishek
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F tripty-etal-2024-cuetsentimentsillies
%X Sentiment analysis (SA) on social media reviews has become a challenging research agenda in recent years due to the exponential growth of textual content. Although several effective solutions are available for SA in high-resourced languages, it is considered a critical problem for low-resourced languages. This work introduces an automatic system for analyzing sentiment in Tamil and Tulu code-mixed languages. Several ML (DT, RF, MNB), DL (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLM-RoBERTa, m-BERT) are investigated for SA tasks using Tamil and Tulu code-mixed textual data. Experimental outcomes reveal that the transformer-based models XLM-R and m-BERT surpassed others in performance for Tamil and Tulu, respectively. The proposed XLM-R and m-BERT models attained macro F1-scores of 0.258 (Tamil) and 0.468 (Tulu) on test datasets, securing the 2ⁿd and 5^th positions, respectively, in the shared task.
%U https://aclanthology.org/2024.dravidianlangtech-1.39
%P 234-239
Markdown (Informal)
[CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based Approach for Sentiment Analysis in Tamil and Tulu Code-Mixed Texts](https://aclanthology.org/2024.dravidianlangtech-1.39) (Tripty et al., DravidianLangTech-WS 2024)
ACL
- Zannatul Tripty, Md. Nafis, Antu Chowdhury, Jawad Hossain, Shawly Ahsan, Avishek Das, and Mohammed Moshiul Hoque. 2024. CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based Approach for Sentiment Analysis in Tamil and Tulu Code-Mixed Texts. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 234–239, St. Julian's, Malta. Association for Computational Linguistics.