I use a computational linguistic algorithm to measure the topics covered in textual descriptions of wine. I ask whether there is information in the text that consumers value. Wine is a prominent example of an experience good. There is substantial product differentiation in the market, and consumers only have limited information on the utility they will receive when consumed. Thus, information is expected to be valuable. Evaluating descriptions of wine produced across the United States, I use a hedonic price regression to explore whether the descriptions provide any new information not already available to the consumer. Initial results suggest that text descriptions are shown to lose their explanatory value when varietal and numerical ratings are included as controls. I then show that once the varietal, region, and numerical ratings are adequately controlled for, there is information in the descriptions that consumers value. (JEL Classifications: C81, D83, L15)
