1. Towards Automating the Pricing Power of Product Attributes: An Analysis of Online Product Reviews
Nikolay Archak,
Anindya Ghose,
Panagiotis Ipeirotis
NYU Stern, IOMS department

2. Word-of-Mouth
   This camera really is a waterproof pocket camera, and it removes worries of rain, sand, mud, or other substances getting into the camera and ruining it. Its very carefree in that way. Unfortunately, it does such a lousy job of exposing, focusing, and capturing that in place of your worry about rain damage is your new worry about capturing that special moment. 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. It even comes with a nice padded belt case. But after you have seen the results, all you will be thinking about as you frame that next shot is if you'll get home and discover the picture you just took is so bad its unusable. …  Comment | Was this review helpful to you?  (Report this) (Report this)

3. Was this review helpful to you?
Bickart & Schindler (2001)
Consumer reviews
User oriented (describe usage scenarios)
Always subjective and may not cover all product characteristics
More credible and trustworthy for consumers
Vendor information
Product oriented (describes technical features)
More objective and complete
Less credible and trustworthy for consumers

4. Is great better than excellent?
Can we assess strength of a consumer review opinion quantitatively?
Yes, if we can measure impact of this opinion on the product demand.
Finally, we can use consumer reviews to predict future product sales and improve our pricing strategy.

5. Hedonic goods and hedonic regressions
U(good) = U(feature1, feature2,…,featuren)
Are all goods hedonic?
Hedonic regressions:
log(Demand) = const + elasticity * log(Price) + b1*feature1 + b2*feature2 + … + bn*featuren
Traditionally used by BLS for CPI calculations
Who decides which features to include and how to measure them? BLS official, not consumers.

6. Word-of-Mouth
   This camera really is a waterproof pocket camera, and it removes worries of rain, sand, mud, or other substances getting into the camera and ruining it. Its very carefree in that way. Unfortunately, it does such a lousy job of exposing, focusing, and capturing that in place of your worry about rain damage is your new worry about capturing that special moment. 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. It even comes with a nice padded belt case. But after you have seen the results, all you will be thinking about as you frame that next shot is if you'll get home and discover the picture you just took is so bad its unusable. …  Comment | Was this review helpful to you?  (Report this) (Report this)

7. Related Work – 1 (Evaluating Polarity of Consumer Reviews)
Looking for occurrences of sentiment phrases
Manually constructed dictionaries (Sanjiv & Chen)
WordNet (Hu & Liu)
Search engines (Turney)
Using machine learning classifiers (Pang, Lee & Vaityanathan)
Naïve Bayes
Max Entropy
SVM
Performance was not as good as in standard text classification. Main issue: consumer review heterogeneity

8. Related Work – 2 (Identifying Product Features)
Solution for heterogeneity problem: identifying product features on which consumers expressed their opinions
Feature-based summaries (Hu & Liu):
Feature: picture quality
– Positive: 97 (“It is easy to use and produces great pictures.”)
– Negative: 10 (“Indoors, this camera is horrible…”)
Feature: size
– Positive: opinion count (“sample sentence”)
– Negative: opinion count (“sample sentence”)

9. Related Work - 3 (“is great better than excellent?”)
Identifying strength of the opinion
Counting scale - Hu & Liu
Supervised learning to classify opinions by subjectivity (weak, medium or strong) – Wilson, Wiebe & Hwa
Our approach – measure impact of consumer reviews on product sales

10. Dataset Product Category “Audio & Video” “Camera & Photo”
Number of products
127
115
Number of observations
35,143
31,233
Number of reviews
2,580
1,955
Period
April 2005 – May 2006

11. Extending classic model of product demand

12. Feature Selection Use Part-Of-Speech tagger to process consumer review
Select frequent nouns and adjectives
Manually process them to select
Evaluation words: “good”, “bad”, “excellent”, “amazing”, “poor”…
Feature words: “lens”, “size”, “quality”, “lcd”…

13. Some issues of feature selection
Implicit features:
“A little noisy in low light, for example on cloudy days, grass will lack sharpness and end up looking like a big mass of green.”
Contents
“i just god married and me and my wife got this camera for our honey moon. we went to england and brugge the trip was great and so was the camera i paired it up with a 1 gig stick and took fulll rez pics the whole trip. bout 400 fit on the disk. only about 3 of the pics turned out fuzzy witch is an extreamly good ratio with the imig stabolization i thought. i came from using a crumy digatal without it will never go back. i am going to get another panasonic in the next few months for my wife to carry in her purce in replace of the one she has in ther right now. going to get one of the ultra compact ones but def going with panasonic again great product and thanks ofr the great shiping amazon”.

14. Feature Selection - Example
As far as plusses, the camera is super high quality, and is relatively easy to use. The lens is fantastic, and the rest of the camera seems to be as equal in quality. I’ve gotten used to the LCD viewfinder, and have been able to use it for some fantastic shots that I might not of otherwise been able to view with the fixed viewfinder... To summarize, this is a very high quality product, well worth the money.
Nx – opinion phrase frequency
Wx – opinion phrase weight
s – smoothing factor

15. Space of consumer reviews
Basis:
For example, fi = ‘quality’, ej = ‘high’
Each linear functional Ψ has basis representation:
Too many parameters: m*n
Solution: place a rank constraint
Special case (p = 1): independent features and evaluations

16. Variables Observation Product Date Consumer reviews Sales rank (Skt)
Price (P1kt)
Discount (P2kt)
Average consumer rating (Rkt)
Product
Consumer reviews
Date
Contents (Wkt)
Rating
Technical characteristics

17. Model Evaluation weights Feature weights Consumer review information
Regularization coefficient

18. Validation (product holdout)
Take away 10% of products and train the model
Predict sales rank for removed products
No-text
Text, regularized
No-text, AR(1)
Text, AR(1)
Audio & Video
2.1486
2.0553
2.2539
1.8666
Camera & Photo
1.0608
1.0392
0.9917
0.8806

19. Validation (observation holdout)
Train the model on data before
Predict sales rank for remaining dates (4 month)
Take into account autocorrelation:
No-text
Text, regularized
No-text, AR(1)
Text, AR(1)
Audio & Video
2.1306
1.9994
1.7197
1.4875
Camera & Photo
1.9489
1.8449
1.637
1.3789

20. 10-fold cross-validation (product holdout)
Average RMSE improvement 5%
Feature
Weight
Evaluation
color
0.4014
best
size
0.3145
decent
resolution
0.2409
nice
quality
0.2040
good
shot(s)
0.2034
excellent
battery(ies)
0.1992
bad
lens(es)
0.1917
poor
photo(s)
0.1899
fine…
screen
0.1770
...perfect
8.0991
camera
0.156
fantastic
5.1222