Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health
Krishnapriya Vishnubhotla, Daniela Teodorescu, Mallory J Feldman, Kristen Lindquist, Saif M. Mohammad
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.
BibTeX
@inproceedings{vishnubhotla-etal-2024-emotion-granularity,
title = "Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health",
author = "Vishnubhotla, Krishnapriya and
Teodorescu, Daniela and
Feldman, Mallory J and
Lindquist, Kristen and
Mohammad, Saif M.",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1069/",
doi = "10.18653/v1/2024.emnlp-main.1069",
pages = "19168--19185"
}