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Panos Ipeirotis Stern School of Business New York University Opinion Mining Using Econometrics.

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Presentation on theme: "Panos Ipeirotis Stern School of Business New York University Opinion Mining Using Econometrics."— Presentation transcript:

1 Panos Ipeirotis Stern School of Business New York University Opinion Mining Using Econometrics

2 Comparative Shopping

3

4 Are Customers Irrational? $11.04 (+1.5%) BuyDig.com gets Price Premium (customers pay more than the minimum price)

5 Price Premiums / Discounts @ Amazon Are Buyers Irrational (?) (paying more) Are Sellers Irrational (?) (charging less)

6 Why not Buying the Cheapest? You buy more than a product  Customers do not pay only for the product  Customers also pay for a set of fulfillment characteristics  Delivery  Packaging  Responsiveness  … Customers care about reputation of sellers!

7 Example of a reputation profile

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9 The Idea in a Single Slide Conjecture: Price premiums measure reputation Reputation is captured in text feedback Our contribution: Examine how text affects price premiums (and learn to rank opinion phrases as a side effect) ACL 2007

10 Decomposing Reputation Is reputation just a scalar metric?  Previous studies assumed a “monolithic” reputation  Decompose reputation in individual components  Sellers characterized by a set of fulfillment characteristics (packaging, delivery, and so on) What are these characteristics (valued by consumers?)  We think of each characteristic as a dimension, represented by a noun, noun phrase, verb or verbal phrase (“shipping”, “packaging”, “delivery”, “arrived”)  We scan the textual feedback to discover these dimensions

11 Decomposing and Scoring Reputation Decomposing and scoring reputation  We think of each characteristic as a dimension, represented by a noun or verb phrase (“shipping”, “packaging”, “delivery”, “arrived”)  The sellers are rated on these dimensions by buyers using modifiers (adjectives or adverbs), not numerical scores  “Fast shipping!”  “Great packaging”  “Awesome unresponsiveness”  “Unbelievable delays”  “Unbelievable price” How can we find out the meaning of these adjectives?

12 Measuring Reputation Regress textual reputation against price premiums Example for “delivery”: –Fast delivery vs. Slow delivery: +$7.95 –So “fast” is better than “slow” by a $7.95 margin

13 Some Indicative Dollar Values Positive Negative Natural method for extracting sentiment strength and polarity good packaging -$0.56 Naturally captures the pragmatic meaning within the given context captures misspellings as well Positive? Negative ?

14 Feature Weights for Digital Cameras SLRPoint & Shoot

15 Other Applications Financial news and price/variance prediction Hotel search and personalization Measuring (and predicting) importance of political events Deriving better keyword bidding, pricing, and ad generation strategies

16 Thank you! Questions? panos@nyu.edu http://economining.stern.nyu.edu

17 Overflow

18 Charts

19 Price premiums @ Amazon

20 Average price premiums @ Amazon

21 Relative Price Premiums

22 Average Relative Price Premiums

23 Data Overview  Panel of 280 software products sold by Amazon.com X 180 days  Data from “used goods” market  Amazon Web services facilitate capturing transactions  We do not use any proprietary Amazon data (Details in the paper)

24 Data: Secondary Marketplace

25 Data: Capturing Transactions time Jan 1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 We repeatedly “crawl” the marketplace using Amazon Web Services While listing appears  item is still available  no sale

26 Data: Capturing Transactions time Jan 1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 We repeatedly “crawl” the marketplace using Amazon Web Services When listing disappears  item sold

27 Capturing Transactions

28 Capturing transactions and “price premiums” Data: Transactions Seller ListingItemPrice When item is sold, listing disappears

29 Capturing transactions and “price premiums” Data: Transactions While listing appears, item is still available time Jan 1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10

30 Capturing transactions and “price premiums” Data: Transactions While listing appears, item is still available time Jan 1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10 Item still not sold on 1/7

31 Capturing transactions and “price premiums” Data: Transactions When item is sold, listing disappears time Item sold on 1/9 Jan 1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan 8 Jan 9 Jan 10

32 Reputation Tools

33 Seller: uCameraSite.com 1.Canon Powershot x300 2.Kodak - EasyShare 5.0MP 3.Nikon - Coolpix 5.1MP 4.Fuji FinePix 5.1 5.Canon PowerShot x900 Reputation Pricing Tool for Sellers Your last 5 transactions in Cameras Name of productPrice Seller 1 - $431 Seller 2 - $409 You - $399 Seller 3 - $382 Seller 4-$379 Seller 5-$376 Canon Powershot x300 Your competitive landscape Product Price ( reputation ) (4.8) (4.65) (4.7) (3.9) (3.6) (3.4) Your Price: $399 Your Reputation Price: $419 Your Reputation Premium: $20 (5%) $20 Left on the table

34 Quantitatively Understand & Manage Seller Reputation RSI Tool for Seller Reputation Management How your customers see you relative to other sellers: 35%* 69% 89% 82% 95% Service Packaging Delivery Overall Quality Dimensions of your reputation and the relative importance to your customers: Service Packaging Delivery Quality Other * Percentile of all merchants RSI Products Automatically Identify the Dimensions of Reputation from Textual Feedback Dimensions are Quantified Relative to Other Sellers and Relative to Buyer Importance Sellers can Understand their Key Dimensions of Reputation and Manage them over Time Arms Sellers with Vital Info to Compete on Reputation Dimensions other than Low Price.

35 Marketplace Search Buyer’s Tool Used Market (ex: Amazon) Price Range $250-$300 Seller 1Seller 2 Seller 4Seller 3 Sort by Price/Service/Delivery/other dimensions Canon PS SD700 Service Packaging Delivery Price Dimension Comparison Seller 1 PriceServicePackageDelivery Seller 2 Seller 3 Seller 4 Seller 5 Seller 6 Seller 7


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