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. CommentComment | Was this review helpful to you? (Report this) (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. Examine changes in demand and estimate weights of features and strength of evaluations. Overview of our Approach “poor lenses” +3% “excellent lenses” -1% “poor photos” +6% “excellent photos” -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 + b 1 *NumMegapixels + b 2 *Zoom + b 3 *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) N x – opinion phrase frequency W x – opinion phrase weight s – smoothing factor excellentpoorgood ejej lenses 0.200.1... photos 000.3… ease of use 00.40… fifi ……… W ij Evaluations Features Matrix [tensor] representation allows us naturally estimate feature weights and evaluation scores.

9. Our Model log (Demand) = a + b* log (Price) + b 1 * Megapixels + b 2 * Zoom + … Ψ 11 *W[“excellent lenses”] + Ψ 12 *W[“great lenses”] +... + Ψ 1M *W[“terrible lenses”] + Ψ 21 *W[“excellent photos”] + Ψ 22 *W[“great photos”] + … + Ψ 2M *W[“terrible photos”] + … Ψ N1 *W[“excellent size”] + Ψ N2 *W[“great size”] +... + Ψ NM *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 Product Category “Audio & Video” “Camera & Photo” Number of products 127115 Number of sales rank observations 35,14331,233 Number of reviews 2,5801,955 PeriodApril 2005 – May 2006

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

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

14. Partial effects for “Camera & Photo” great camera0.4235decent battery-0.0139 good camera0.1128decent quality-0.0822 great quality0.0931poor quality-0.1067 good quality0.0385bad camera-0.6547 great battery0.0138fine camera-0.677 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)