Measuring and Understanding the Impact of Emotional Granularity on Coping with Everyday Unpleasant Service Experiences

Measuring and Understanding the Impact of Emotional Granularity on Coping with Everyday Unpleasant Service Experiences: A Deep Learning Approach

Abstract:
When describing their emotions, people may demonstrate high granularity by differentiating between emotions when using emotional labels, or low granularity by using emotion labels interchangeably to indicate general valance. We develop a computational method to analyze over 11 million online reviews from over 52,000 reviewers to investigate whether the granularity with which a person describes their negative emotions from an unpleasant experience predicts their likelihood of coping with that experience. Using reviewers’ rating of the business as a proxy for coping, we observe that describing negative emotions more granularly in the review text is associated with better coping. Granularity in describing positive emotions does not produce similar results. Findings suggest that beyond trait-level variances in emotional granularity, situation-level variances in how emotions are described also impact emotion regulation and coping. The computational method developed to assess emotional granularity in language use may be applied to unobtrusively measure emotional granularity in settings beyond online reviews.

More information on Prof. Ali Tamaddoni can be found here

The Seminar will be held in Seminar Room RuW 1.201 as well as broadcasted via Zoom