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Large scale comparative analyses of hotel photo content posted by managers and customers to review platforms based on deep learning: implications for hospitality marketers
journal contribution
posted on 2021-01-01, 00:00 authored by Meng Ren, Quan VuQuan Vu, Gang LiGang Li, Rob LawWith 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.
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Journal
Journal of hospitality marketing and managementVolume
30Issue
1Pagination
96 - 119Publisher
Taylor & FrancisLocation
Abingdon, Eng.Publisher DOI
ISSN
1050-7051eISSN
1936-8631Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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Hotel photocontent analysisvisual featuredeep learningcomputer visionSocial SciencesBusinessHospitality, Leisure, Sport & TourismManagementBusiness & EconomicsSocial Sciences - Other TopicsSOCIAL NETWORK ANALYSISDESTINATION IMAGESTOURISM DESTINATIONPURCHASE INTENTIONSNEURAL-NETWORKSVISUAL CONTENTCLASSIFICATIONMACHINEPREFERENCESQUALITY
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