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

Abstract 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.