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        <title>DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method</title>
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            <title>Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion</title>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Depression is linked to the development of dementia. Cognitive functions such as thinkingand remembering generally deteriorate in dementiapatients. Social media usage has beenincreased among the people in recent days. Thetechnology advancements help the communityto express their views publicly. Analysing thesigns of depression from texts has become animportant area of research now, as it helps toidentify this kind of mental disorders among thepeople from their social media posts. As part ofthe shared task on detecting signs of depressionfrom social media text, a dataset has been providedby the organizers (Sampath et al.). Weapplied different machine learning techniquessuch as Support Vector Machine, Random Forestand XGBoost classifier to classify the signsof depression. Experimental results revealedthat, the XGBoost model outperformed othermodels with the highest classification accuracyof 0.61% and an Macro F1 score of 0.54.</abstract>
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%0 Conference Proceedings
%T DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method
%A Sharen, Herbert
%A Rajalakshmi, Ratnavel
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F sharen-rajalakshmi-2022-dlrg
%X Depression is linked to the development of dementia. Cognitive functions such as thinkingand remembering generally deteriorate in dementiapatients. Social media usage has beenincreased among the people in recent days. Thetechnology advancements help the communityto express their views publicly. Analysing thesigns of depression from texts has become animportant area of research now, as it helps toidentify this kind of mental disorders among thepeople from their social media posts. As part ofthe shared task on detecting signs of depressionfrom social media text, a dataset has been providedby the organizers (Sampath et al.). Weapplied different machine learning techniquessuch as Support Vector Machine, Random Forestand XGBoost classifier to classify the signsof depression. Experimental results revealedthat, the XGBoost model outperformed othermodels with the highest classification accuracyof 0.61% and an Macro F1 score of 0.54.
%R 10.18653/v1/2022.ltedi-1.53
%U https://aclanthology.org/2022.ltedi-1.53
%U https://doi.org/10.18653/v1/2022.ltedi-1.53
%P 346-349

Markdown (Informal)

[DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method](https://aclanthology.org/2022.ltedi-1.53) (Sharen & Rajalakshmi, LTEDI 2022)

• DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method (Sharen & Rajalakshmi, LTEDI 2022)

ACL

• Herbert Sharen and Ratnavel Rajalakshmi. 2022. DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 346–349, Dublin, Ireland. Association for Computational Linguistics.