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        <title>Poorvi@DravidianLangTech: Sentiment Analysis on Code-Mixed Tulu and Tamil Corpus</title>
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        <namePart type="given">Poorvi</namePart>
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            <title>Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages</title>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="given">Ruba</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Sentiment analysis in code-mixed languages poses significant challenges, particularly for highly under-resourced languages such as Tulu and Tamil. Existing corpora, primarily sourced from YouTube comments, suffer from class imbalance across sentiment categories. Moreover, the limited number of samples in these corpus hampers effective sentiment classification. This study introduces a new corpus tailored for sentiment analysis in Tulu code-mixed texts. The research applies standard pre-processing techniques to ensure data quality and consistency and handle class imbalance. Subsequently, multiple classifiers are employed to analyze the sentiment of the code-mixed texts, yielding promising results. By leveraging the new corpus, the study contributes to advancing sentiment analysis techniques in under-resourced code-mixed languages. This work serves as a stepping stone towards better understanding and addressing the challenges posed by sentiment analysis in highly under-resourced languages.</abstract>
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%0 Conference Proceedings
%T Poorvi@DravidianLangTech: Sentiment Analysis on Code-Mixed Tulu and Tamil Corpus
%A Shetty, Poorvi
%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 shetty-2023-poorvi
%X Sentiment analysis in code-mixed languages poses significant challenges, particularly for highly under-resourced languages such as Tulu and Tamil. Existing corpora, primarily sourced from YouTube comments, suffer from class imbalance across sentiment categories. Moreover, the limited number of samples in these corpus hampers effective sentiment classification. This study introduces a new corpus tailored for sentiment analysis in Tulu code-mixed texts. The research applies standard pre-processing techniques to ensure data quality and consistency and handle class imbalance. Subsequently, multiple classifiers are employed to analyze the sentiment of the code-mixed texts, yielding promising results. By leveraging the new corpus, the study contributes to advancing sentiment analysis techniques in under-resourced code-mixed languages. This work serves as a stepping stone towards better understanding and addressing the challenges posed by sentiment analysis in highly under-resourced languages.
%U https://aclanthology.org/2023.dravidianlangtech-1.16
%P 124-132

Markdown (Informal)

[Poorvi@DravidianLangTech: Sentiment Analysis on Code-Mixed Tulu and Tamil Corpus](https://aclanthology.org/2023.dravidianlangtech-1.16) (Shetty, DravidianLangTech-WS 2023)

• Poorvi@DravidianLangTech: Sentiment Analysis on Code-Mixed Tulu and Tamil Corpus (Shetty, DravidianLangTech-WS 2023)

ACL

• Poorvi Shetty. 2023. Poorvi@DravidianLangTech: Sentiment Analysis on Code-Mixed Tulu and Tamil Corpus. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 124–132, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.