EmoMent: An Emotion Annotated Mental Health Corpus from Two South Asian Countries

Proceedings of the 29th International Conference on Computational Linguistics, pages 6991–7001 October 12–17, 2022. 6991 EmoMent: An Emotion Annotated Mental Health Corpus from two South Asian Countries Thushari Atapattu1, Mahen Herath2, Charith Elvitigala3, Piyanjali de Zoysa4, Kasun Gunawardane3, Menasha Thilakaratne1, Kasun de Zoysa3 and Katrina Falkner1 1School of Computer Science, The University of Adelaide, Australia 2Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka 3University of Colombo School of Computing, Sri Lanka 4Department of Psychology, University of Colombo, Sri Lanka email: thushari.atapattu@adelaide.edu.au Abstract People often utilise online media (e.g. Face- book, 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. sad- ness) 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 sen- tences) extracted from two South Asian coun- tries - Sri Lanka and India. Three clinical psy- chology postgraduates were involved in anno- tating these posts into eight categories, includ- ing mental illness (e.g. depression) and emo- tions (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. 1 Introduction Mental health issues remain a leading cause for poor well-being and suicide. The World Health Organisation (WHO) indicates that 400 million people are affected by mental disorders such as depression, resulting in a cost of US$ 1 trillion per year from the global economy allocated for de- pression and anxiety disorders alone (WHO, 2019; James et al., 2018). Recent research using AI and NLP demonstrates strong potential to automatically detect mental health issues from digital footprints such that professionals could provide timely inter- ventions and mental health resources to vulnerable persons. These data contain useful information to understand patients’ distressed state of mind out- side a traditional clinical environment. Research suggests that mental health issues are reflected in the ‘emotions’ (e.g. sadness, anger) indicated in one’s expression of language. De- spite the popularity of research studies in detecting mental disorders using online data such as Twit- ter (Coppersmith et al., 2014, 2015; Cohan et al., 2018) and emotion modeling (Strapparava and Mi- halcea, 2007; Mohammad et al., 2018; Demszky et al., 2020; Oberländer and Klinger, 2018), the au- tomated identification of the association between emotions and mental disorders have largely being ignored, apart from a recent study (CEASE corpus (Ghosh et al., 2020)) that focused on the role of emotions on suicidal ideation. Motivated by this, we introduce a novel, emotion-annotated mental health (EmoMent) cor- pus1 using Facebook posts extracted …

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