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

Show me the Money! Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Nikolay Archak , Anindya Ghose, Panagiotis Ipeirotis New York University Stern School of Business Information Systems Group, IOMS department. Word of “Mouse ”. Consumer reviews

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

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  1. Show me the Money! Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Nikolay Archak, Anindya Ghose, Panagiotis Ipeirotis New York University Stern School of Business Information Systems Group, IOMS department

  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? Sales data provides valuable clues

  5. Overview of our Approach • Examine changes in demand and estimate weights of features and strength of evaluations. “excellent lenses” “excellent photos” +3% +6% “poor lenses” “poor photos” -1% -2% • Feature “photos” is twice more important than “lenses” • “Excellent” is positive, “poor” is negative • “Excellent” is three times stronger than “poor”

  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) Evaluations Features Nx – opinion phrase frequency Wx – opinion phrase weight s – smoothing factor Matrix [tensor] representation allows us naturally estimate feature weights and evaluation scores.

  9. Our Model log (Demand) = a + b* log (Price) + b1* Megapixels + b2* Zoom + … Ψ11*W[“excellent lenses”] + Ψ12*W[“great lenses”] + ... + Ψ1M*W[“terrible lenses”] + Ψ21*W[“excellent photos”] + Ψ22*W[“great photos”] + … + Ψ2M*W[“terrible photos”] + … ΨN1*W[“excellent size”] + ΨN2*W[“great size”] + ... + ΨNM*W[“terrible size”]

  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” Partial effect of an opinion phrase: score of the “average review” where all evaluations of the feature f are replaced by the evaluation e minus score of the “average review”.

  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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