Online persuasion of review emotional intensity: A text mining analysis of restaurant reviews

By Hengyun Li, Hongbo Liu, Zili Zhang. 2020

103 citations recorded by the source corpus.

Abstract Consumer-generated restaurant reviews are important sources in consumers’ purchase decisions. The purpose of this study is to explore the impact of emotional intensity on perceived review usefulness as well as the moderating effects of review length and reviewer expertise. Data from 600,686 reviews of 300 popular restaurants in the US were obtained from Yelp. Using a text mining approach and econometric analysis, empirical results show that (1) positive emotional intensity has a negative impact on perceived review usefulness, whereas negative emotional intensity has a positive impact on perceived review usefulness; (2) among the two most prevalent discrete negative emotions in online reviews (i.e., anger and anxiety), reviews expressing anger are more useful than those expressing anxiety; and (3) review length and reviewer expertise can moderate the effect of emotional intensity on perceived review usefulness.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3936992bd148a07cfcff07364d80256dcc4fbb2c