Coping through Precise Labeling of Emotions: A Deep Learning Approach to Studying Emotional Granularity in Consumer Reviews

Abstract

When describing their emotions, people may demonstrate emotional expertise by differentiating between emotions when using emotional labels or use emotion labels interchangeably to indicate a general valence. The authors develop a novel deep-learning-based method to measure the granularity with which people describe their emotions via language. They investigate the role of emotional granularity in consumer decision making, specifically in relation to coping with negative consumption experiences described in online reviews. Granularity in describing negative emotions is associated with more successful coping with negative experiences. Therefore, especially when the overall experience is negative, in which case coping is most relevant, greater granularity in describing negative emotions predicts more positive ratings of the business. Furthermore, in line with the view that the ability to granularly describe negative emotions is a skill, reviewers progressively become more granular when describing their negative emotions as they write more reviews. Consequently, reviewers progressively provide more positive ratings for negative experiences as they write more reviews. Finally, a greater temporal distance between the consumption experience and the writing of the review predicts greater granularity in describing negative emotions. Consequently, when the overall experience is negative and coping is relevant, a greater temporal distance predicts more positive ratings.