From Text to Vision: Examining How Emotional Expression and Information Consistency Affect Perceived Helpfulness

This study used natural language processing tools and econometric analysis methods to examine the impact of emotional expressions on the helpfulness of online reviews. This study conducts a sentiment analysis of 71,850 reviews collected by Yelp.com in Los Angeles, California, United States, and divides them into positive and negative according to the valence of the text content. It examines the direct impact of reviews with different valences on the reviews’ perceived helpfulness and clarifies how the information consistency among review cues, that is, the inconsistency between a single review rating and the average merchant rating, and the consistency between the review content and rating moderate this effect. Results revealed that rating inconsistency significantly amplifies the positive effect of negative reviews and the negative effect of positive reviews on helpfulness. Greater consistency in review content and ratings reduced the negative effect of positive reviews on perceived helpfulness but did not significantly moderate the perceived helpfulness of negative reviews. Additionally, research has examined the moderating role of visual information in enhancing the helpfulness of reviews, indicating that visual elements such as pictures significantly affect the perceived helpfulness of reviews. Accordingly, this study enriches our understanding of online review dynamics and identifies practical strategies for online review platforms and social media to leverage user-generated content to optimize consumer experience and satisfaction.