Assessing the helpfulness of online hotel reviews: A classification-based approach. Issue 2 (May 2018) - Lee, Pei-Ju; Hu, Ya-Han; Lu, Kuan-Ting
Highlights: This study proposes a novel review helpfulness prediction model of hotel reviews. Review quality, review sentiment, and reviewer characteristics are considered as predictors. More than 1.1 million hotel reviews were collected fromTripAdvisor.com to evaluate the proposed model. The selected features can be used to develop effective prediction models for assessing review helpfulness. Abstract: With the rapid development of Web 2.0, travelers have started sharing their travel experiences on websites. The expanding amount of online hotel reviews results in the problem of information overload. Therefore, the effective identification of helpful reviews has become an important research issue. In this study, online hotel reviews were collected fromTripAdvisor.com, and the helpfulness of these reviews was comprehensively investigated from the aspects of review quality, review sentiment, and reviewer characteristics. Review helpfulness prediction models were also developed by using classification techniques. The results indicate that reviewer characteristics are good predictors of review helpfulness, whereas review quality and review sentiment are poor predictors of review helpfulness.