1. Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews
- Author
-
Nelleke de Boer, Frederik Situmeang, and Austin Zhang
- Subjects
Marketing ,Economics and Econometrics ,Computer science ,business.industry ,Research methodology ,05 social sciences ,Latent Dirichlet allocation ,Data science ,symbols.namesake ,Text mining ,Product reviews ,0502 economics and business ,symbols ,050211 marketing ,Customer satisfaction ,Business and International Management ,business ,050203 business & management - Abstract
The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.
- Published
- 2019