Recognizing Supporting Standpoints for Online Restaurant Reviews: Evidence From Tripadvisor

This article presents a practical framework that analyzes a real dataset from Tripadvisor with 2330 restaurant reviews. The reviews are written in English, and the structure of the reviews is free, not following any specific form. We conducted four experiments that exploited different artificial intelligence models and natural language processing systems. Our results indicate that an online restaurant review often shares several standpoints with another review of the same restaurant. Our results also illustrate that the number of agreed standpoints between reviews is not related to the rating of the respective reviews. Likewise, the results imply that the pattern of variation in the number of reviews (identified as supported with a specific evidence intensity) is unrelated to the reviews’ rating, where ‘‘evidence intensity’’ refers to the number of shared standpoints between a review and its supporter review. Additionally, if a restaurant review is found unsupported, the cause is mainly related to how the review was structured and written. Our framework can be utilized by online review platforms, such as Tripadvisor, to accredit or rank reviews based on supporting evidence from other related reviews.