Assessing the helpfulness of online hotel reviews: A classification-based approach
Telematics and InformaticsPublished 5 January 2018
Pei-Ju Lee, Ya‐Han Hu, Kuan‐Ting Lu
Citations161
SJR quartileQ1
SJR score2.13
SNIP2.78
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TL;DR
Review helpfulness prediction models indicate that reviewer characteristics are good predictors of review helpfulness, whereas review quality and review sentiment are poor predictor ofreview 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 from TripAdvisor.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.
Keywords
Social SciencesDecision SciencesComputer Science
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