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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/28516
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| Title: | Deriving the Pricing Power of Product Features by Mining Consumer Reviews |
| Authors: | Archak, Nikolay - NYU Stern School of Business
Ghose, Anindya - NYU Stern School of Business
Ipeirotis, Panagiotis - NYU Stern School of Business |
| Keywords: | consumer reviews, e-commerce, econometrics, electronic commerce,
electronic markets, hedonic analysis, Internet, opinion mining, product
review, sentiment analysis, text mining, user-generated content |
| Issue Date: | 2007 |
| Series/Report no.: | NET Institute Working Paper;07-36 |
| 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/2451/28516 |
| Appears in Collections: | NET Institute Working Papers Series
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