Sentiment Classification of Consumer-Generated Online Reviews Using Topic Modeling

Ana Catarina Calheiros, Sérgio Moro, Paulo Rita

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)

Abstract

The development of the Internet and mobile devices enabled the emergence of travel and hospitality review sites, leading to a large number of customer opinion posts. While such comments may influence future demand of the targeted hotels, they can also be used by hotel managers to improve customer experience. In this article, sentiment classification of an eco-hotel is assessed through a text mining approach using several different sources of customer reviews. The latent Dirichlet allocation modeling algorithm is applied to gather relevant topics that characterize a given hospitality issue by a sentiment. Several findings were unveiled including that hotel food generates ordinary positive sentiments, while hospitality generates both ordinary and strong positive feelings. Such results are valuable for hospitality management, validating the proposed approach.

Original languageEnglish
Pages (from-to)675-693
Number of pages19
JournalJournal of Hospitality Marketing and Management
Volume26
Issue number7
DOIs
Publication statusPublished - 3 Oct 2017

Keywords

  • Customer reviews
  • hospitality
  • sentiment classification
  • text mining
  • topic modeling

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