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Abstract Details
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Activity Number:
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40
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #304824 |
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Title:
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Online User Reviews and the Evolution of Perceived Quality
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Author(s):
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Peter Lenk*+
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Companies:
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University of Michigan
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Address:
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701 Tappan, Ann Arbor, MI, 48109-1217, United States
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Keywords:
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Dynamic Linear Model ;
Finite Mixture Heterogeniety ;
User Generated Content ;
Text Analysis ;
Quality
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Abstract:
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We consider online user-generated reviews and ratings in a context where the underlying quality of the product or service is changing over time. Previous studies of the effects of online ratings have been in the context of a fixed product, such as books or movies. In our case of restaurant reviews, product quality changes over time. We capture time dynamics with a latent DLM and reviewer heterogeneity with a finite mixture model. We test our model with data on 200 restaurants in San Francisco for which we have most reviews posted online since 1997 at various web sites. Our data includes both a 1-5 overall rating as well as the sentiment scores extracted from their written reviews. The sentiment scores were generated by an automatic sentiment analyzer that derives the valence and strength of sentiments in a review regarding the food, ambience, and service quality of the restaurants.
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Authors who are presenting talks have a * after their name.
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