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        <title>Selam@DravidianLangTech:Sentiment Analysis of Code-Mixed Dravidian Texts using SVM Classification</title>
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        <namePart type="given">Selam</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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            <namePart type="given">Anand</namePart>
            <namePart type="given">Kumar</namePart>
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            <namePart type="given">Sajeetha</namePart>
            <namePart type="family">Thavareesan</namePart>
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        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Sherly</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Sentiment analysis in code-mixed text written in Dravidian languages. Specifically, Tamil- English and Tulu-English. This paper describes the system paper of the RANLP-2023 shared task. The goal of this shared task is to develop systems that accurately classify the sentiment polarity of code-mixed comments and posts. be provided with development, training, and test data sets containing code-mixed text in Tamil- English and Tulu-English. The task involves message-level polarity classification, to classify YouTube comments into positive, negative, neutral, or mixed emotions. This Code- Mix was compiled by RANLP-2023 organizers from posts on social media. We use classification techniques SVM and achieve an F1 score of 0.147 for Tamil-English and 0.518 for Tulu- English.</abstract>
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%0 Conference Proceedings
%T Selam@DravidianLangTech:Sentiment Analysis of Code-Mixed Dravidian Texts using SVM Classification
%A Kanta, Selam
%A Sidorov, Grigori
%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 kanta-sidorov-2023-selam
%X Sentiment analysis in code-mixed text written in Dravidian languages. Specifically, Tamil- English and Tulu-English. This paper describes the system paper of the RANLP-2023 shared task. The goal of this shared task is to develop systems that accurately classify the sentiment polarity of code-mixed comments and posts. be provided with development, training, and test data sets containing code-mixed text in Tamil- English and Tulu-English. The task involves message-level polarity classification, to classify YouTube comments into positive, negative, neutral, or mixed emotions. This Code- Mix was compiled by RANLP-2023 organizers from posts on social media. We use classification techniques SVM and achieve an F1 score of 0.147 for Tamil-English and 0.518 for Tulu- English.
%U https://aclanthology.org/2023.dravidianlangtech-1.24
%P 176-179

Markdown (Informal)

[Selam@DravidianLangTech:Sentiment Analysis of Code-Mixed Dravidian Texts using SVM Classification](https://aclanthology.org/2023.dravidianlangtech-1.24) (Kanta & Sidorov, DravidianLangTech-WS 2023)

• Selam@DravidianLangTech:Sentiment Analysis of Code-Mixed Dravidian Texts using SVM Classification (Kanta & Sidorov, DravidianLangTech-WS 2023)

ACL

• Selam Kanta and Grigori Sidorov. 2023. Selam@DravidianLangTech:Sentiment Analysis of Code-Mixed Dravidian Texts using SVM Classification. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 176–179, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.