Sentiment classification of online reviews to travel destinations by supervised machine learning approaches

Qiang Ye, Ziqiong Zhang, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

390 Citations (Scopus)

Abstract

The rapid growth in Internet applications in tourism has lead to an enormous amount of personal reviews for travel-related information on the Web. These reviews can appear in different forms like BBS, blogs, Wiki or forum websites. More importantly, the information in these reviews is valuable to both travelers and practitioners for various understanding and planning processes. An intrinsic problem of the overwhelming information on the Internet, however, is information overloading as users are simply unable to read all the available information. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they needed about specific destinations. The returned pages from these search engines are still beyond the visual capacity of humans. In this research, sentiment classification techniques were incorporated into the domain of mining reviews from travel blogs. Specifically, we compared three supervised machine learning algorithms of Naïve Bayes, SVM and the character based N-gram model for sentiment classification of the reviews on travel blogs for seven popular travel destinations in the US and Europe. Empirical findings indicated that the SVM and N-gram approaches outperformed the Naïve Bayes approach, and that when training datasets had a large number of reviews, all three approaches reached accuracies of at least 80%.
Original languageEnglish
Pages (from-to)6527-6535
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number3 PART 2
DOIs
Publication statusPublished - 1 Apr 2009

Keywords

  • Online reviews
  • Sentiment classification
  • Supervised machine learning algorithm
  • Travel destinations

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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