Large-scale comparative analyses of hotel photo content posted by managers and customers to review platforms based on deep learning: implications for hospitality marketers

Meng Ren, Huy Quan Vu, Gang Li, Rob Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


With the prevalence of social media and Web 2.0, online visual contents such as photos or videos have quickly evolved into one popular information-disseminating channel for hotel managers and travelers. The current study aims to obtain a comprehensive understanding of the preconceptions as reflected in online photos posted by travelers. This paper presents a novel approach to online photo content analysis based on deep learning theory and computer vision framework, which can comprehensively analyze the content of large-scale photo datasets. We demonstrate and evaluate this approach through a case study, wherein we analyze over 53,000 photos collected from hotel review platform, TripAdvisor. We identified interesting differences in the contents of photos posted by hotel managers and travelers, including the differences in photo contents between low- and high-rating hotels. Our findings provide valuable implication for hotel marketing using visual assets.

Original languageEnglish
JournalJournal of Hospitality Marketing and Management
Publication statusAccepted/In press - 2020


  • computer vision
  • content analysis
  • deep learning
  • Hotel photo
  • visual feature

ASJC Scopus subject areas

  • Management Information Systems
  • Tourism, Leisure and Hospitality Management
  • Marketing

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