Abstract
Abstract As many readers struggle with massive textual information on review websites, developing optimized recommender systems that assist readers in identifying relevant reviews is critical. The present study aims to explore and predict the relationship between a reviewer’s evaluation of distinct attributes (i.e., importance and sentiment of a restaurant aspect) 2 and overall satisfaction (i.e., generic numerical rating of a restaurant). Latent Aspect Rating Analysis is modified to achieve the goal. The study identifies five restaurant attributes: food & drinks, customer service, dining atmosphere, restaurant value, and location. Restaurant value contributes most from the importance perspective and food & drinks contributes most from the sentiment perspective. Restaurant value ranks the first as the overall satisfaction of attributes (i.e., combination of importance and sentiment). Accordingly, the present study suggests a supplement of the “dynamic” recommender systems. This study offers scholars and practitioners a refined approach to analyze wealthy review content.
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