Tourism forecasting with granular sentiment analysis

Hengyun Li, Huicai Gao and Haiyan Song

Annals of Tourism Research, 2023, vol. 103, issue C

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.

Keywords: Deep learning; Multisource Internet big data; Tourism demand forecasting; Fine-grained sentiment analysis; Hybrid feature engineering (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:anture:v:103:y:2023:i:c:s0160738323001408

DOI: 10.1016/j.annals.2023.103667

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