Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning

• Published in: International journal of hospitality management 2018-04, Vol.71, p.120-131
• Main Authors: Ma, Yufeng, Xiang, Zheng, Du, Qianzhou, Fan, Weiguo
• Format: Article
• Language: English
• Subjects: Computer vision; Deep learning; Hotel; Online reviews; Social media analytics; User-provided photos
• ISSN: 0278-4319; 1873-4693
• DOI: 10.1016/j.ijhm.2017.12.008

•We introduce of deep learning in natural language processing and particularly computer visions to hospitality and tourism management.•We conducted an analytics exercise using deep learning algorithms to predict review helpfulness based on textual and visual contents in online hotel reviews.•Results show user-provided photos complement textual contents in predicting review helpfulness.•We discuss the possible applications of deep learning techniques in hospitality and tourism literature within the big data contexts. Online reviews have been extensively studied in the hospitality and tourism literature. However, while user-provided photos embedded in online reviews accumulate in large quantities, their informational value has not been well understood likely due to technical challenges. The goal of this study is to introduce deep learning for computer vision to understand information value of online hotel reviews. Using a dataset collected from two social media sites, we compared deep learning models with other machine learning techniques to examine the effect of user-provided photos on review helpfulness. Findings show that deep learning models were more useful in predicting review helpfulness than other models. While user-provided photos alone did not have the same impact as review texts, combining review texts and user-provided photos produced the highest performance. Implications for the applications of deep learning technologies in hospitality and tourism research, as well as limitations and directions for future research, are discussed.