A talk for SWAIE2012 at EKAW2012 http://semanticweb.cs.vu.nl/swaie2012/ Paper at http://jodischneider.com/pubs/swaie2012.pdf

1. Identifying
Consumers’ Arguments in Text
Jodi Schneider1 and Adam Wyner2
1 - Digital Enterprise Research Institute, National University of Ireland, Galway
2 – Department of Computer Science, University of Liverpool
Tuesday October 9, 2012
SWAIE 2012 (colocated with EKAW 2012)
at National University of Ireland
Galway, Ireland

2. Outline
• Motivation & Goals
• Our Approach
– Provide a Semi-Automated Support Tool
– Use Argumentation Schemes
– Use Information Extraction
• Example Results
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3. Reviews are rich & detailed
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4. Customers disagree,
especially in comments
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5. Customer Questions
• What’s controversial?
• What are some reasons to buy the item? Not to buy it?
• What sorts of people participate in the discussion?
• Are there authorities who can help me decide what to buy?
• Are there people similar to me who like this item? And why?
…Similar people who dislike it? Why?
• What opinions are given about features of the item?
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6. Manufacturer Questions
• What features are controversial?
• What market segments report positive
(negative) experiences?
• What else are customers talking about?
May reveal other customer needs.
– Advice
– Competitor’s products
– Related products to be used in conjunction?
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7. Limited Structure
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8. Goal: A knowledge base we can query
• Who likes this camera?
• What statements are made about particular
camera features?
e.g. indoor picture quality
• Which claims do they support?
e.g. Do they support the claim that
“the camera gives quality indoor pictures”?
Or the opposite claim?
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9. Our approach
• Build a support tool
– semi-automated
– rule-based
– using text analytics
• Use argumentation schemes
– patterns for reasoning
– identify text mining targets for info extraction
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10. Simple Reasoning Pattern
Premises:
• The Canon SX220 has good video quality.
• Good video quality promotes image quality for
casual photographers.
Conclusion:
• Casual photographers should buy the Canon SX220.
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11. Argumentation Scheme
Premises:
• The <camera> has <feature>.
• <feature> promotes <user value> for <user class>.
Conclusion:
• <user class> should <e-commerce action> the
<camera>.
<e-commerce action>: buy, not buy, sell, return, …
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12. Variables as Targets for Information
Extraction
<camera>
<property>
<user value>
<user type>
<e-commerce action>
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13. 4 Argumentation Schemes in the Paper
1. User Classification
2. Camera Classification
3. Appropriateness
4. Consumer Relativised
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14. Building more complex reasoning patterns
• “Cascade” of argumentation schemes
• Conclusions of one scheme as premises for another
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15. Consumer Relativised
Argumentation Scheme
3 Premises:
1. User Class (Conclusion of User Classification AS)
2. Camera Class (Conclusion of Camera Classification AS)
3. Appropriateness (Conclusion of Appropriateness AS)
Conclusion: User should buy Camera
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16. Consumer Relativised
Argumentation Scheme
Premises:
1. Cameras of class Y are appropriate for agents of
class X.
2. Camera y is of class Y.
3. Agent x is of class X.
Conclusion:
Agent x should buy camera y.
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17. Appropriateness Argumentation Scheme
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18. Appropriateness Argumentation Scheme
Premises:
1. Agent x is in user class X.
2. Camera y is in camera class Y.
3. The camera’s contexts of use satisfy the user’s context
of use.
4. The camera’s available features satisfy the user’s
desirable features.
5. The camera’s quality expectations satisfy the user’s
quality expectations.
Conclusion:
Cameras of class Y are appropriate for agents of class X.
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19. Premises become
Information Extraction Targets
Premises of the Appropriateness AS:
1. Agent x is in user class X.
2. Camera y is in camera class Y.
3. The camera’s contexts of use satisfy the user’s
context of use.
4. The camera’s available features satisfy the user’s
desirable features.
5. The camera’s quality expectations satisfy the
user’s quality expectations
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20. Information Extraction
1. User class
2. (Camera class)
3. Contexts of use: camera’s, user’s
4. Features: camera’s available, user’s desirable
5. Quality expectations: camera’s, user’s
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21. Query for patterns
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22. Amazing low light photos
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23. Mainly bright colours in good daylight
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24. Arguments are User Relative
• Amazing low light photos?
• Only for bright colours in good daylight?
•  Motivates the user classification
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25. Future work: argumentation schemes
• Further instantiate the schemes using the tool
– Where do they work well?
– Improvements needed?
• Develop additional schemes
– Expertise
– Comparison
– Particular features (e.g. warranties)
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26. Future work: ontologies & concepts
• Ontologies and reasoning
– Ontology for users
– Ontology for cameras
– Test inferences by importing scheme instances into an
argumentation inference engine.
• Address conceptual issues
– Clarify distinctions between the camera’s quality
expectations and features
– Support matches between a user’s values and camera
properties
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27. Future work: evaluation
• Evaluate the tool
– How well does it support users? (faster, better analyses?)
– Do annotation types match users’ expectations?
(interannotator agreement)
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28. Related Papers
• Talk at EKAW, Thursday 11:45: “Dimensions of
argumentation in social media”
Schneider, Davis, and Wyner (EKAW 2012).
• Wyner, Schneider, Atkinson, and Bench-Capon.
“Semi-Automated Argumentative Analysis of Online Product
Reviews.” In 4th International Conference on Computational
Models of Argument (COMMA 2012).
• Wyner and Schneider (2012). ''Arguing from a point of
view'', Agreement Technologies.
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29. Acknowledgements
• FP7-ICT-2009-4 Programme, IMPACT Project, Grant
Agreement Number 247228.
• Science Foundation Ireland Grant No. SFI/08/CE/I1380 (Líon-
2)
• Short-term Scientific Mission grant from COST Action IC0801
on Agreement Technologies
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30. Thanks for your attention!
• Questions?
• Contacts:
– Jodi Schneider jodi.schneider@deri.org
– Adam Wyner adam@wyner.info
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31. October 9, 2012 Schneider & Wyner, SWAIE at EKAW 2012 31

32. 4 Argumentation Schemes in the Paper
1. User Classification AS
2. Camera Classification AS
3. Appropriateness AS
Concludes: Camera Class is appropriate for User Class
Premises: User Class, Camera Class, User & Camera Match
• Match on: Contexts of Use, Features, Quality Expectations
4. Consumer Relativised AS
Concludes: User should buy Camera
Premises: User Class, Camera Class, Appropriateness
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33. Domain terminology
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34. Find camera features
• Use :
– Has a flash
– Number of megapixels
– Scope of the zoom
– Lens size
– The warranty
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35. Find argument passages
after, as, because, for, since, when, ....
• C
therefore, in conclusion, consequently, ....
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36. Argument indicators:
Premise & Conclusion
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37. To find attacks between arguments
• Use contrast terminology:
– Indicators
but, except, not, never, no, ....
– Contrasting sentiment
The flash worked .
The flash worked .
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38. Sentiment terminology
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39. ,
,
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40. User Classification argumentation scheme
Variables are our targets for extraction.
Premises:
Agent x…
1. … has user’s attributes aP1; aP2; …
2. … user’s context of use aU1; aU2; …
3. … has user’s desirable camera features aF1; aF2; ...
4. … has user’s quality expectations aQ1; aQ2; ...
5. … has user’s values aV1; aV2; ...
6. …has desirable camera features aF1; aF2; … promote/demote
user’s values aV1; aV2; ...
Conclusion:
Agent x is in class X.
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41. An argument for buying the camera
Premises:
The pictures are perfectly exposed.
The pictures are well-focused.
No camera shake.
Good video quality.
Each of these properties promotes image quality.
Conclusion:
(You, the reader,) should buy the CanonSX220.
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42. An argument for NOT buying the
camera
Premises:
The colour is poor when using the flash.
The images are not crisp when using the flash.
The flash causes a shadow.
Each of these properties demotes image quality.
Conclusion:
(You, the reader,) should not buy the CanonSX220.
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43. Counterarguments to the premises of
“Don’t buy”
The colour is poor when using the flash.
For good colour, use the colour setting, not the flash.
The images are not crisp when using the flash.
No need to use flash even in low light.
The flash causes a shadow.
There is a corrective video about the flash shadow.
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44. Making sense of reviews
• Do other reviews agree?
– Any counterarguments?
• Is this point relevant to me?
– Does this reviewer have similar needs?
– Does it apply in my situation?
• Is enough information provided?
– Any explanations?
– Any examples?
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