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http://ssrn.com/abstract=1024903
 
 

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Deriving the Pricing Power of Product Features by Mining Consumer Reviews


Nikolay Archak


New York University (NYU) - Leonard N. Stern School of Business

Anindya Ghose


New York University - Leonard N. Stern School of Business

Panagiotis G. Ipeirotis


New York University - Leonard N. Stern School of Business

February 10, 2010

Management Science, Forthcoming
NET Institute Working Paper No. 07-36

Abstract:     
Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted and hence, the textual content of product reviews is an important determinant of consumers’ choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique dataset from Amazon, containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers’ relative preferences for different product features and, also, how text can be used for predictive modeling of future changes in sales.

Number of Pages in PDF File: 36

Keywords: user-generated content, consumer reviews, e-commerce, econometrics, electronic markets, sentiment analysis, text mining

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Date posted: October 27, 2007 ; Last revised: May 6, 2011

Suggested Citation

Archak, Nikolay and Ghose, Anindya and Ipeirotis, Panagiotis G., Deriving the Pricing Power of Product Features by Mining Consumer Reviews (February 10, 2010). Management Science, Forthcoming; NET Institute Working Paper No. 07-36. Available at SSRN: http://ssrn.com/abstract=1024903

Contact Information

Nikolay Archak
New York University (NYU) - Leonard N. Stern School of Business ( email )
44 West 4th Street
New York, NY NY 10012
United States
Anindya Ghose (Contact Author)
New York University - Leonard N. Stern School of Business ( email )
44 West 4rth Street
New York, NY 10012
United States
Panagiotis G. Ipeirotis
New York University - Leonard N. Stern School of Business ( email )
44 West Fourth Street
Ste 8-84
New York, NY 10012
United States
+1-212-998-0803 (Phone)
HOME PAGE: http://www.stern.nyu.edu/~panos
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