Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation

This paper identifies the key dimensions of customer service voiced by hotel visitors use a data mining approach, latent dirichlet analysis (LDA), which uncovers 19 controllable dimensions that are key for hotels to manage their interactions with visitors.

Abstract

Consumer-generated content has provided an important new information medium for tourists, throughout the purchasing lifecycle, transforming the way that visitors evaluate, select and share experiences about tourism. Research in this area has largely focused on quantitative ratings provided on websites. However, advanced techniques for linguistic analysis provide the opportunity to extract meaning from the valuable comments provided by visitors. In this paper, we identify the key dimensions of customer service voiced by hotel visitors use a data mining approach, latent dirichlet analysis (LDA). The big data set includes 266,544 online reviews for 25,670 hotels located in 16 countries. LDA uncovers 19 controllable dimensions that are key for hotels to manage their interactions with visitors. We also find differences according to demographic segments. Perceptual mapping further identifies the most important dimensions according to the star-rating of hotels. We conclude with the implications of our study for future research and practice.