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. Yelp.com) better utilize their review information to monitor business performance and inform consumer choice.
I am a fourth-year PhD candidate in the Decision, Operations and Information Technologies (DO&IT) Department at the Robert H. Smith School of Business at the University of Maryland. I hold a BS in Computer Engineering and a MS in Electronic Engineering from Georgia Institute of Technology.
My research focuses on social contagion -- measuring and managing how information diffusion affects consumer behavior and business outcomes. My advisors are Anand Gopal and Shawn Mankad. For a larger profile, please visit a recent profile from the University of Maryland.