Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114090
Title: User-generated photos in hotel demand forecasting
Authors: Xu, J
Zhang, W
Li, H 
Zheng, XK 
Zhang, J
Issue Date: Sep-2024
Source: Annals of tourism research, Sept 2024, v. 108, 103820
Abstract: User-generated content has become an invaluable resource for researchers in hospitality and tourism, especially regarding sales and demand forecasting. Some scholars have analyzed textual data and sentiment information; however, few studies have addressed roles of user-generated photos in hotel demand prediction. This study fills this void by examining the effectiveness of various photo features (i.e., topics and sentiments) for hotel demand forecasting. Results demonstrate the superiority of photo topic features over sentiment features in enhancing demand prediction. Forecasting accuracy is further improved after integrating a combination of photo topic and sentiment features. Moreover, user-generated photos elevate the accuracy of daily demand forecasting for different hotels. This study contributes to the literature on hotel demand forecasting using Internet multimodal data.
Keywords: Hotel demand forecasting
Multimodal data
Online review
User-generated photos
Publisher: Pergamon Press
Journal: Annals of tourism research 
ISSN: 0160-7383
EISSN: 1873-7722
DOI: 10.1016/j.annals.2024.103820
Appears in Collections:Journal/Magazine Article

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