The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors
Puranam, D., V. Narayan and V. Kadiyali (2017), "The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors," Marketing Science, Accepted.
45 Pages Posted: 20 Jul 2017
Date Written: July 17, 2017
In 2008, New York City mandated that all chain restaurants post calorie information in their menus. For managers of chain and standalone restaurants, as well as for policy makers, a pertinent goal might be to monitor the impact of this regulation on consumer conversations. We propose a scalable Bayesian topic model to measure and understand changes in consumer opinion about health (and other topics). We calibrate the model on 761,962 online reviews of restaurants posted over 8 years. Our model allows managers to specify prior topics of interest such as “health” for a calorie posting regulation. It also allows the distribution of topic proportions within a review to be affected by its length, valence and the experience level of its author. Using a difference-in-difference estimation approach, we isolate the potentially causal effect of the regulation on consumer opinion. Following the regulation, there was a statistically small but significant increase in the proportion of discussion of the health topic. This increase can be attributed largely to authors who did not post reviews before the regulation, suggesting that the regulation prompted several consumers to discuss health in online restaurant reviews.
Keywords: Online reviews, text mining, calories, Affordable Health Care Act
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