Optimization of Consumer Satisfaction Towards Tourism Attractions through Text Mining

Abstract

With the ascension of tourism e-commerce, a growing number of consumers are placing greater emphasis on consumer satisfaction and posting their travel experiences on tourism e-commerce platform. Processing the online information generated by consumers through text mining has also become a key technology applied in E-commerce. In order to identify the factors that impact consumer satisfaction and find the method to optimize consumer satisfaction, this study collected data from Ctrip.com and applied topic modeling and regressions to online reviews of tourism attractions. The results show that: (1) consumers with higher involvement in tourism websites are more likely to have lower satisfaction with tourism attractions. (2) online reviews that focus more on Price, Entrance and tickets tend to have lower satisfaction, while online reviews that contain more about Scenery, History and culture, Entertainment and Service are more likely to be higher on consumer satisfaction. These findings show that text mining can be instrumental for E-commerce to optimize management strategies.