Abstract
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 language | English |
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Pages (from-to) | 1092-1107 |
Number of pages | 16 |
Journal | Asia Pacific Journal of Tourism Research |
Volume | 24 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2 Nov 2019 |
Keywords
- 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