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Show me the Money Deriving the Pricing Power of Product Features by Mining Consumer Reviews.

Word of Mouse". Consumer reviewsDerived from user experienceDescribe different product featuresProvide subjective evaluations of product features I love virtually everything about this camera....except the lousy picture quality. The camera looks great, feels nice, is easy to use, starts up qu

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Show me the Money Deriving the Pricing Power of Product Features by Mining Consumer Reviews.

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    2. Word of “Mouse” Consumer reviews Derived from user experience Describe different product features Provide subjective evaluations of product features I love virtually everything about this camera....except the lousy picture quality. The camera looks great, feels nice, is easy to use, starts up quickly, and is of course waterproof. It fits easily in a pocket and the battery lasts for a reasonably long period of time. Comment | Was this review helpful to you?  (Report this) (Report this)

    3. Existing work Identifying product features Hu, Liu (AAAI, 2004) Ghani, Probst, Liu, Krema, Fano (KDD, 2006) Scaffidi (2006) Sentiment classification Das, Chen (2001) Turney, Littman (ACL, 2003) Dave, Lawrence, Pennock (WWW, 2003) Hu, Liu (KDD, 2004) Popescu, Etzioni, (EMNLP, 2005) Opinion Analysis Hu, Liu, Cheng (WWW, 2005)

    4. Research Questions How important is each product feature to customers? What is the pragmatic polarity and strength of customers’ opinions?

    5. Examine changes in demand and estimate weights of features and strength of evaluations. Overview of our Approach

    6. Economic background – Hedonic goods and hedonic regressions We are not the first to measure weights of product features. Economists are doing this for years. Hedonic goods [Rosen, 1974]: Each good is characterized by the set of its objectively measured features Preferences of consumers are solely determined by features of available goods Are all goods hedonic? Hedonic regressions: log(CameraPrice) = const + b1*NumMegapixels + b2*Zoom + b3*StorageSize +…

    7. Hedonic regressions with subjectively measured features Problem: traditional hedonic regressions include only objectively measured features Our solution: introduce review evaluations into the hedonic framework. Each opinion assigns implicit subjective score to a feature [We don’t know the scores]. For example: review1 says “excellent lenses” [implicit opinion score: 0.7] and “nice lenses” [implicit opinion score: 0.3] review2 says “decent lenses” [implicit opinion score: -0.1] Average score of the “lenses” feature is: [0.7 + 0.3 - 0.1] / 3 = 0.3

    8. Representing consumer review(s)

    9. Our Model

    10. Technical Challenge – Reduce the Number of Parameters Solution: place a rank constraint Special case (p = 1): independent features weights and evaluation scores

    11. Amazon.com Dataset

    12. Results - Feature Weights for “Camera & Photo”

    13. Results - Evaluation Coefficients for “Camera & Photo”

    14. Partial effects for “Camera & Photo”

    15. Predictive power of product reviews Goal: predict future sales using review text Model test: 10-fold cross validation (product holdout) Compared with model that ignores text but keeps numeric variables including average review rating Average RMSE improvement 5%, Avg. Err improvement 3%

    16. Conclusions We provided technique for: Measuring importance of product features for consumers Identifying polarity and strength of user evaluations Alleviating problem of data sparseness

    17. Thank you! Comments? Questions?

    18. Related Work Chevalier, Mayzlin (2006) Chevalier, Goolsbee (2003) Ghani, Probst, Liu, Krema, Fano (2006) Hu, Liu (2004) Hu, Liu, Cheng (2005) Turney (2002) Pang, Lee (2005) Popescu, Etzioni (2005)

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