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http://hdl.handle.net/10397/107300
Title: | Tourism forecasting with granular sentiment analysis | Authors: | Li, H Gao, H Song, H |
Issue Date: | Nov-2023 | Source: | Annals of tourism research, Nov. 2023, v. 103, 103667 | 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. © 2023 Elsevier Ltd | Keywords: | Deep learning Fine-grained sentiment analysis Hybrid feature engineering Multisource Internet big data Tourism demand forecasting |
Publisher: | Elsevier Ltd | Journal: | Annals of tourism research | ISSN: | 0160-7383 | EISSN: | 1873-7722 | DOI: | 10.1016/j.annals.2023.103667 |
Appears in Collections: | Journal/Magazine Article |
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