Invited Lecture: Cornell University

April 24, 2015

More Than Just Words: On Discovering Themes in Online Reviews to Explain
Restaurant Closures

Abstract: Online reviews and their effect on business outcomes have long been of interest to information
systems scholars. In this study, we complement the existing research on online reviews by
proposing a novel use of modern text analysis methods to uncover the semantic structure of
online reviews and assess their impact on the survival of merchants in the marketplace. We
analyze online reviews from 2005 to 2013 for restaurants in a major metropolitan area in the
United States and find that variables capturing semantic structure within review text are
important predictors of the survival of restaurants, a relationship that has not been explored in the
extant literature. We refer to these semantic dimensions as service themes. We thus combine
machine learning approaches and econometric modeling to derive predictive models that are
significantly better than models that simply include numerical information from reviews such as
review valence, volume, word counts and readability. Our results suggest that our text mining
methodology, if and when applied to production-level environments on large datasets, can
extract valuable information pertaining to these themes from the online reviews generated by
consumers. The products of such techniques can help business managers (e.g. restaurateurs) and
platform owners (e.g. better utilize their review information to monitor business
performance and inform consumer choice.