Examining destination images from travel blogs: a big data analytical approach using latent Dirichlet allocation

Rui Wang, Jin Xing Hao, Rob Law, Jun Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


In the big data era, destination images have played an increasingly important role in tourism development. However, seldom tourism research has utilised big data analytics to examine destination images from travel blogs. Therefore, this study proposes and evaluates a big data analytical approach using latent Dirichlet allocation to extract attributes of online destination images from 140,286 travel blogs about 20 cities in China. Results reveal 14 dimensions with 54 attributes of destination images of the studied cities. Interesting findings are discovered between online destination images and tourism cities. This study also summarises the implications for tourism research and practice.

Original languageEnglish
Pages (from-to)1092-1107
Number of pages16
JournalAsia Pacific Journal of Tourism Research
Issue number11
Publication statusPublished - 2 Nov 2019


  • big data analytics
  • China
  • correspondence analysis
  • destination management
  • latent Dirichlet allocation
  • online destination image
  • social media
  • tourism marketing
  • Travel blog
  • user-generated contents

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Tourism, Leisure and Hospitality Management

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