@inproceedings{atapattu-etal-2022-emoment,
title = "{E}mo{M}ent: An Emotion Annotated Mental Health Corpus from Two {S}outh {A}sian Countries",
author = "Atapattu, Thushari and
Herath, Mahen and
Elvitigala, Charitha and
de Zoysa, Piyanjali and
Gunawardana, Kasun and
Thilakaratne, Menasha and
de Zoysa, Kasun and
Falkner, Katrina",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.609",
pages = "6991--7001",
abstract = "People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person{'}s choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent),consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including {`}mental illness{'} (e.g., depression) and emotions (e.g., {`}sadness{'}, {`}anger{'}). EmoMent corpus achieved {`}very good{'} inter-annotator agreement of 98.3{\%} (i.e. {\%} with two or more agreement) and Fleiss{'} Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.",
}
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%0 Conference Proceedings
%T EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries
%A Atapattu, Thushari
%A Herath, Mahen
%A Elvitigala, Charitha
%A de Zoysa, Piyanjali
%A Gunawardana, Kasun
%A Thilakaratne, Menasha
%A de Zoysa, Kasun
%A Falkner, Katrina
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F atapattu-etal-2022-emoment
%X People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person’s choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent),consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including ‘mental illness’ (e.g., depression) and emotions (e.g., ‘sadness’, ‘anger’). EmoMent corpus achieved ‘very good’ inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss’ Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.
%U https://aclanthology.org/2022.coling-1.609
%P 6991-7001
Markdown (Informal)
[EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries](https://aclanthology.org/2022.coling-1.609) (Atapattu et al., COLING 2022)
ACL
- Thushari Atapattu, Mahen Herath, Charitha Elvitigala, Piyanjali de Zoysa, Kasun Gunawardana, Menasha Thilakaratne, Kasun de Zoysa, and Katrina Falkner. 2022. EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6991–7001, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.