Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107300
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | - |
dc.creator | Li, H | - |
dc.creator | Gao, H | - |
dc.creator | Song, H | - |
dc.date.accessioned | 2024-06-13T07:07:46Z | - |
dc.date.available | 2024-06-13T07:07:46Z | - |
dc.identifier.issn | 0160-7383 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107300 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Fine-grained sentiment analysis | en_US |
dc.subject | Hybrid feature engineering | en_US |
dc.subject | Multisource Internet big data | en_US |
dc.subject | Tourism demand forecasting | en_US |
dc.title | Tourism forecasting with granular sentiment analysis | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Title in author's file: Tourism demand forecasting using sentiment analysis | en_US |
dc.identifier.volume | 103 | - |
dc.identifier.doi | 10.1016/j.annals.2023.103667 | - |
dcterms.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 | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | Annals of tourism research, Nov. 2023, v. 103, 103667 | - |
dcterms.isPartOf | Annals of tourism research | - |
dcterms.issued | 2023-11 | - |
dc.identifier.scopus | 2-s2.0-85174696623 | - |
dc.identifier.eissn | 1873-7722 | - |
dc.identifier.artn | 103667 | - |
dc.description.validate | 202406 bcch | - |
dc.identifier.FolderNumber | a2810 | en_US |
dc.identifier.SubFormID | 48437 | en_US |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2026-11-30 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Page views
4
Citations as of Jun 30, 2024
SCOPUSTM
Citations
6
Citations as of Jun 21, 2024
WEB OF SCIENCETM
Citations
5
Citations as of Jun 27, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.