Data Analysis of Tourists’ Online Reviews on Restaurants in a Chinese Website

Abstract

The proliferation of online consumer reviews has led to more people choosing where to eat based on these reviews, especially when they visit an unfamiliar place. While previous research has mainly focused on attributes specific to restaurant reviews and takes aspects such as food quality, service, ambience, and price into consideration, this study aims to identify new attributes by analyzing restaurant reviews and examining the influence of these attributes on star ratings of a restaurant to figure out the factors influencing travelers’ preferences for a particular restaurant. In order to achieve this research goal, this study analyzed Chinese tourists’ online reviews on Korean restaurants on dianping.com, the largest Chinese travel website. The text mining method, including the LDA topic model and R statistical software, will be used to analyze the review text in depth. This study will academically contribute to the existing literature on the field of the hospitality and tourism industry and practically provide ideas to restaurant owners on how to attract foreign customers by managing critical attributes in online reviews. © 2020, Springer Nature Switzerland AG.