How do Mainland Chinese tourists perceive Hong Kong in turbulence? A deep learning approach to sentiment analytics

Jin Xing Hao, Rui Wang, Rob Law, Yan Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


Deep learning has garnered increasing attention in many research fields. However, prior research seldom focused on tourists' perception prediction and prescription towards tourism destinations in turbulence. This study attempts to fill the gap by investigating Mainland Chinese tourists' perception of a turbulent Hong Kong society through deep learning-based sentiment analytics. This incorporates a convolutional neural network (CNN) model for sentiment prediction and feature frequency analysis for sentiment prescription for 52,950 Chinese travel microblogs about Hong Kong. Results show that the CNN-based deep learning approach can obtain improved sentiment predictive performance with minimal domain knowledge and human effort. The trend of Mainland Chinese tourists' (MCTs') sentiments about Hong Kong and the unique sentiment features revealed by our approach can provide practitioners with new insights into design customised tourism marketing and development strategies. The MCTs' shared mentalities emerged from sentiment features may help to enhance our theoretical knowledge about tourists' perception in turbulent tourism markets.

Original languageEnglish
Pages (from-to)478-490
Number of pages13
JournalInternational Journal of Tourism Research
Issue number4
Publication statusPublished - 1 Jul 2021


  • automated sentiment analytics
  • China
  • deep learning
  • Hong Kong
  • travel microblogs
  • turbulent markets

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Nature and Landscape Conservation


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