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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 text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from www.expedia.com and www.we8there.com . Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0993 .
Büschken et al. (Mon,) studied this question.