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Deriving the Pricing Power of Product Features by Mining Consumer Reviews

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Author Info

  • Nikolay Archak

    ()
    (Leonard Stern School of Business, New York University, New York, New York 10012)

  • Anindya Ghose

    ()
    (Leonard Stern School of Business, New York University, New York, New York 10012)

  • Panagiotis G. Ipeirotis

    ()
    (Leonard Stern School of Business, New York University, New York, New York 10012)

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    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 data set 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. This paper was accepted by Ramayya Krishnan, information systems.

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    File URL: http://dx.doi.org/10.1287/mnsc.1110.1370
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 57 (2011)
    Issue (Month): 8 (August)
    Pages: 1485-1509

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    Handle: RePEc:inm:ormnsc:v:57:y:2011:i:8:p:1485-1509

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    Related research

    Keywords: Bayesian learning; consumer reviews; discrete choice; electronic commerce; electronic markets; opinion mining; sentiment analysis; user-generated content; text mining; econometrics;

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    Cited by:
    1. Joe Cox & Daniel Kaimann, 2013. "The Signaling Effect of Critics - Evidence from a Market for Experience Goods," Working Papers CIE 68, University of Paderborn, CIE Center for International Economics.
    2. Weijia Dai & Ginger Z. Jin & Jungmin Lee & Michael Luca, 2012. "Optimal Aggregation of Consumer Ratings: An Application to Yelp.com," NBER Working Papers 18567, National Bureau of Economic Research, Inc.
    3. repec:pdn:wpaper:68 is not listed on IDEAS
    4. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    5. repec:pdn:ciepap:84 is not listed on IDEAS
    6. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2013. "Platform or Wholesale? Different Implications for Retailers of Online Product," Working Papers 13-14, NET Institute.

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