Emotion Granularity from Text: An Aggregate-Level Indicator of Mental
  Health
Krishnapriya Vishnubhotla Daniela Teodorescu Mallory J. Feldman Kristen A. Lindquist Saif M. Mohammad
We are united in how emotions are central to shaping our experiences; and
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.