1. Show Me the Money! Deriving the Pricing Power of Product Features by Mining Consumer Reviews Nikolay Archak, Anindya Ghose, and Panagiotis G. Ipeirotis Leonard N. Stern School of Business, New York University SIGKDD 2007

2. 2007/11/06 Chia-ying Lee2 Introduction Consumer-generated product information attracts more interest than vendor information Early work performed relatively well but failed to achieve high accuracy This work focus on the weight customer value on the feature and the evaluation

3. 2007/11/06 Chia-ying Lee3 Goal Estimate the weight that customers place on each individual product feature Estimate the implicit evaluation score that customers assign to each feature Estimate how these evaluations affect the revenue for a given product.

4. 2007/11/06 Chia-ying Lee4 Hedonic Regression Assumes that differentiated goods can be described by vectors of objectively measured features and the consumer ’ s valuation estimate the value that different product aspects contribute to a consumer ’ s utility

5. 2007/11/06 Chia-ying Lee5 Mining Consumer Opinion 1. A feature mining technique is used to identify product features 2. The algorithms extract sentences that give (positive or negative) opinions for a product feature 3. A summary is produced using the discovered information Fail to identify the strength of the evaluations and the importance of the feature

6. 2007/11/06 Chia-ying Lee6 Econometric Opinion Mining Identifying customer opinions Notation and Symbol descriptions Structuring the opinion phrase space Econometric model of product reviews

7. 2007/11/06 Chia-ying Lee7 Identifying Customer Opinions Noun as feature Adjective as evaluation Noun-adjective as opinion phrases Only consider frequently appeared term Features vector X = (ef 1,..., ef n )

8. 2007/11/06 Chia-ying Lee8 Notation and Symbol descriptions

9. 2007/11/06 Chia-ying Lee9 Structuring the Opinion Phrase Space Feature space as F ( dim F = n) Evaluation space as E (dim E = m) Review space R as R = F ⊗ E Weight of opinion phrase (standard term frequency measure)

10. 2007/11/06 Chia-ying Lee10 Example Reviews: “ The camera is of high quality and relatively easy to use. The lens are fantastic! I have been able to use the LCD viewfinder for some fantastic shots... To summarize, this is a very high quality product ” Tensor product = 0.4 · (quality ⊗ high) + 0.2 · (use ⊗ easy) + 0.2 · (lens ⊗ fantastic) +0.2 · (shots ⊗ fantastic) (assuming s = 0)

11. 2007/11/06 Chia-ying Lee11 Econometric Model of Product Reviews 1/5 A simple econometric linear model: Dkt : the demand for the product k at time t pkt : its price at time t β : the price elasticity ak :a product specific constant term which captures the unobserved product heterogeneity Εkt: represents a random disturbance factor (normally distributed)

12. 2007/11/06 Chia-ying Lee12 Econometric Model of Product Reviews 2/5 Assume that qualitative consumer reviews correspond to some quantitative evaluations of product characteristics Wkt ∈ R that captures opinions for product k, including all reviews before time t a: time and product invariant constant

13. 2007/11/06 Chia-ying Lee13 Econometric Model of Product Reviews 3/5 Average the weights for each opinion phrase until time t ψ(x) = ψ(f i ⊗ e j ) is the value of the functional on the basis vector f i ⊗ e j

14. 2007/11/06 Chia-ying Lee14 Econometric Model of Product Reviews 4/5 Use Singular Value Decomposition to reduce model complexity rank-1 approximation means each adjective has one meaning, independently of the feature

15. 2007/11/06 Chia-ying Lee15 Econometric Model of Product Reviews 5/5 Maximum likelihood estimate 1. Set δ to a vector of initial feature weights 2. Minimize the fit function by choosing the optimal γ assuming that δ are fixed 3. Minimize the fit function by choosing the optimal δ assuming thatγ 4. Repeat Step 2 and 3, until the algorithm converges May converge to some local minimum of the fit function

16. 2007/11/06 Chia-ying Lee16 Experimental Evaluation Data set Selecting feature and evaluation words Experimental setup Experimental results

17. 2007/11/06 Chia-ying Lee17 Data Set 1/2 242 products from Amazon.com during March 2005 to May 2006 (15-month) 20 reviews per product Categoryproductsobservationsreviews Camera and Photo115312331955 Audio and Video127351432580

18. 2007/11/06 Chia-ying Lee18 Data Set 2/2 Review: numerical rating (1-5stars) the date the actual text posted by the reviewer Observation : the average product rating the date the product ID the price (including discount) suggested retail price the sales rank …

19. 2007/11/06 Chia-ying Lee19 Selecting Feature and Evaluation Words 1.Part-of-speech tagger (Stanford NLP group) 2.Extracted frequent nouns. Manually select 30 nouns subset as product features (ex: lens/lenses, screen/lcd/display) 3.Syntactic dependency parser (Stanford NLP group) Choosing 30 most frequent adjectives evaluated the selected product features

20. 2007/11/06 Chia-ying Lee20 Experimental Setup Sales rank (S) as demand Include both the suggested retail price (P1) and the price on Amazon.com (P2) Include the review rating variable (R)

21. 2007/11/06 Chia-ying Lee21 Experimental Results 1/5 Predicting Future Sales Used 10-fold cross-validation to test the model ’ s performance and obtain reliable coefficient estimates Compared performance for predicting sales rank, using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) 5% improvement in RMSE and 3% improvement in MAE

22. 2007/11/06 Chia-ying Lee22 Experimental Results 2/5 Estimating Product Feature Weights and Evaluation Scores

23. 2007/11/06 Chia-ying Lee23 Experimental Results 3/5

24. 2007/11/06 Chia-ying Lee24 Experimental Results 4/5

25. 2007/11/06 Chia-ying Lee25 Experimental Results 5/5 Evaluation Conclusions Results show that we can identify the features that are important to consumers Weak positive opinions like nice and decent are actually evaluated in a negative manner in electronic markets

26. 2007/11/06 Chia-ying Lee26 Thank You!