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        <title>JudithJeyafreeda_StressIdent_LT-EDI@EACL2024: GPT for stress identification</title>
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        <namePart type="given">Judith</namePart>
        <namePart type="given">Jeyafreeda</namePart>
        <namePart type="family">Andrew</namePart>
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            <title>Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion</title>
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        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Bharathi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Buitelaar</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Thenmozhi</namePart>
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        <name type="personal">
            <namePart type="given">György</namePart>
            <namePart type="family">Kovács</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Miguel</namePart>
            <namePart type="given">Ángel</namePart>
            <namePart type="family">García Cumbreras</namePart>
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    <abstract>Stress detection from social media texts has proved to play an important role in mental health assessments. People tend to express their stress on social media more easily. Analysing and classifying these texts allows for improvements in development of recommender systems and automated mental health assessments. In this paper, a GPT model is used for classification of social media texts into two classes - stressed and not-stressed. The texts used for classification are in two Dravidian languages - Tamil and Telugu. The results, although not very good shows a promising direction of research to use GPT models for classification.</abstract>
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        <date>2024-03</date>
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            <start>173</start>
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%0 Conference Proceedings
%T JudithJeyafreeda_StressIdent_LT-EDI@EACL2024: GPT for stress identification
%A Andrew, Judith Jeyafreeda
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F andrew-2024-judithjeyafreeda
%X Stress detection from social media texts has proved to play an important role in mental health assessments. People tend to express their stress on social media more easily. Analysing and classifying these texts allows for improvements in development of recommender systems and automated mental health assessments. In this paper, a GPT model is used for classification of social media texts into two classes - stressed and not-stressed. The texts used for classification are in two Dravidian languages - Tamil and Telugu. The results, although not very good shows a promising direction of research to use GPT models for classification.
%U https://aclanthology.org/2024.ltedi-1.18
%P 173-176

Markdown (Informal)

[JudithJeyafreeda_StressIdent_LT-EDI@EACL2024: GPT for stress identification](https://aclanthology.org/2024.ltedi-1.18) (Andrew, LTEDI-WS 2024)

• JudithJeyafreeda_StressIdent_LT-EDI@EACL2024: GPT for stress identification (Andrew, LTEDI-WS 2024)

ACL

• Judith Jeyafreeda Andrew. 2024. JudithJeyafreeda_StressIdent_LT-EDI@EACL2024: GPT for stress identification. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 173–176, St. Julian's, Malta. Association for Computational Linguistics.