Latent Topic Modeling of Consumer Reviews: Linking Text Evaluations to Customer Satisfaction and Brands

Abstract

Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for exploratory text analysis that makes use of the sentence structure contained in the reviews, and show that it leads to improved inference and prediction relative to existing models using data from Expedia. The topics associated with different levels of satisfaction and hotel brands are found to be different, more distinguished and more interpretable that those emerging from alternative analysis.