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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/23604
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| Title: | Deriving the Pricing Power of Product Features by Mining Consumer Reviews |
| Authors: | Archak, Nikolay
Ghose, Anindya
Ipeirotis, Panagiotis G. |
| Issue Date: | 8-Oct-2007 |
| Series/Report no.: | CeDER-07-05 |
| Abstract: | The growing pervasiveness of the Internet has changed the way that
consumers shop for goods. Increasingly, user-generated product reviews
serve as a valuable source of information for customers making product
choices online. While there is a significant body of theory on
multi-attribute choice under uncertainty, the literature that examines
product reviews has not built on this stream of theory for a variety of
reasons. Typically, the impact of product reviews has been incorporated
by 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. We provide a text mining
technique that allows us to incorporate text in choice and panel data
models by decomposing textual reviews into segments, evaluating
different product features. We test our approach on a unique dataset
collected from Amazon, and demonstrate how it can be used to learn
consumers' relative preferences for different product features. The
dataset used contains three different groups of products (digital
cameras, camcorders, PDAs), associated sales data and consumer review
data gathered over a 15-month period. Additionally, we present and
discuss two experimental techniques that can be used to alleviate the
problem of data sparsity and of omitted variables: the first technique
models consumer opinions as elements of a tensor product of independent
feature and evaluation spaces and the second technique clusters rare
opinions based on pointwise mutual information. The paper concludes by
discussing the managerial relevance of this work as a tool for
extracting actionable business intelligence from user-generated content. |
| URI: | http://hdl.handle.net/2451/23604 |
| Appears in Collections: | CeDER Working Papers
IOMS: Information Systems Working Papers
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