Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94068
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | - |
dc.creator | Li, H | - |
dc.creator | Ji, H | - |
dc.creator | Liu, H | - |
dc.creator | Cai, D | - |
dc.creator | Gao, H | - |
dc.date.accessioned | 2022-08-11T01:06:51Z | - |
dc.date.available | 2022-08-11T01:06:51Z | - |
dc.identifier.issn | 0261-5177 | - |
dc.identifier.uri | http://hdl.handle.net/10397/94068 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Photo number | en_US |
dc.subject | Photo sentiment | en_US |
dc.subject | Review enjoyment | en_US |
dc.subject | Review usefulness | en_US |
dc.subject | Text–photo sentiment disparity | en_US |
dc.title | Is a picture worth a thousand words? Understanding the role of review photo sentiment and text-photo sentiment disparity using deep learning algorithms | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 92 | - |
dc.identifier.doi | 10.1016/j.tourman.2022.104559 | - |
dcterms.abstract | Images have become integral to consumers' sharing of consumption experiences due to their abilities of carrying rich and vivid information. This study investigates the impacts of restaurant review photo sentiment on customers’ perceived review usefulness and enjoyment using deep learning and econometric model analysis. The results indicate that (1) reviews with photos are more useful and enjoyable than reviews without photos; (2) a U-shaped relationship exists between review photo sentiment and review usefulness, with the effect of review photo sentiment on review enjoyment being positive and linear. Moreover, the effects can be strengthened by the number of review photos while weakened by the text-photo sentiment disparity. The above findings are reinforced by a sample of restaurant online reviews written by tourists in Las Vegas. This study contributes to the electronic word-of-mouth literature as well as to the application of machine learning technologies in computer vision to tourism and hospitality research. | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | Tourism management, Oct. 2022, v. 92, 104559 | - |
dcterms.isPartOf | Tourism management | - |
dcterms.issued | 2022-10 | - |
dc.identifier.scopus | 2-s2.0-85129771198 | - |
dc.identifier.eissn | 1879-3193 | - |
dc.identifier.artn | 104559 | - |
dc.description.validate | 202208 bcch | - |
dc.identifier.FolderNumber | a1553 | en_US |
dc.identifier.SubFormID | 45400 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China (71902169) | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University Departmental General Research Fund (Project No. G-UALR) | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2025-10-31 | en_US |
Appears in Collections: | Journal/Magazine Article |
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