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Tourism forecasting with granular sentiment analysis

Author

Listed:
  • Li, Hengyun
  • Gao, Huicai
  • Song, Haiyan

Abstract

Generic sentiment calculations cannot fully reflect tourists' preferences, whereas fine-grained sentiment analysis identifies tourists' precise attitudes. This study forecasted visitor arrivals at two tourist attractions in China using Internet data from multiple sources. Empirical results indicate that 1) fine-grained sentiment analysis of online review data can substantially improve tourism demand models' forecasting performance; 2) combining multidimensional sentiment analysis–based online review data with search engine data outperforms search engine data in tourism demand prediction; and 3) fine-grained sentiment analysis–based online review data and search engine data maintain stable predictive power during times of uncertainty.

Suggested Citation

  • Li, Hengyun & Gao, Huicai & Song, Haiyan, 2023. "Tourism forecasting with granular sentiment analysis," Annals of Tourism Research, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:anture:v:103:y:2023:i:c:s0160738323001408
    DOI: 10.1016/j.annals.2023.103667
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    Cited by:

    1. Yuanfang Fu & Zhenrao Cai & Chaoyang Fang, 2024. "Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data," Sustainability, MDPI, vol. 16(3), pages 1-24, January.

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