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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 increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for
goods. Consumer-generated product reviews have become a valuable source of information for customers,
who read the reviews and decide whether to buy the product based on the information provided. In this
paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics
of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of
this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to
estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation
score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given
product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics
that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We
then impute the quantitative impact of consumer reviews on product demand as a linear functional from
this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to
significantly reduce the number of model parameters, while still providing good experimental results. We
evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer
reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can
extract actionable business intelligence from the data and better understand the customer preferences and
actions. We also show that the textual portion of the reviews can improve product sales prediction compared
to a baseline technique that simply relies on numeric data. |
| URI: | http://hdl.handle.net/2451/23604 |
| Appears in Collections: | IOMS: Information Systems Working Papers
CeDER Working Papers
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