| Download | - View final version: Emotion granularity from text: an aggregate-level indicator of mental health (PDF, 580 KiB)
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| Link | https://aclanthology.org/2024.emnlp-main.1069/ |
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| Author | Search for: Vishnubhotla, Krishnapriya; Search for: Teodorescu, Daniela; Search for: Feldman, Mallory J; Search for: Lindquist, Kristen; Search for: Mohammad, Saif M.1ORCID identifier: https://orcid.org/0000-0003-2716-7516 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), November 12-16, 2024, Miami, Florida, USA |
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| Abstract | We are united in how emotions are central to shaping our experiences; yet, individuals differ greatly in how we each identify, categorize, and express emotions. In psychology, variation in the ability of individuals to differentiate between emotion concepts is called emotion granularity (determined through self-reports of one’s emotions). High emotion granularity has been linked with better mental and physical health; whereas low emotion granularity has been linked with maladaptive emotion regulation strategies and poor health outcomes. In this work, we propose computational measures of emotion granularity derived from temporally-ordered speaker utterances in social media (in lieu of self reports that suffer from various biases). We then investigate the effectiveness of such text-derived measures of emotion granularity in functioning as markers of various mental health conditions (MHCs). We establish baseline measures of emotion granularity derived from textual utterances, and show that, at an aggregate level, emotion granularities are significantly lower for people self-reporting as having an MHC than for the control population. This paves the way towards a better understanding of the MHCs, and specifically the role emotions play in our well-being. |
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| Publication date | 2024-11-12 |
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| Publisher | Association for Computational Linguistics |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 324dd126-5b85-4d45-892a-09b4192be497 |
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| Record created | 2024-11-14 |
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| Record modified | 2024-11-15 |
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