1. 1 Proximity-Based Opinion Retrieval Mark CarmanFabio CrestaniShima Gerani

2. 2 What is Blog Post Opinion Retrieval?

3. 3 Blog Post Opinion Retrieval Aims at developing an effective retrieval function that ranks posts according to the likelihood that they are expressing an opinion about a particular topic.

4. 4 Relevant Opinion

5. 5 Relevant Opinion

6. 6 A Common Approach to Opinion Retrieval Rank posts by relevance, select the highest ranking posts

7. 7 A Common Approach to Opinion Retrieval Rank posts by relevance, select the highest ranking posts Calculate opinion score for each document

8. 8 A Common Approach to Opinion Retrieval Rank posts by relevance, select the highest ranking posts Combine the opinion and relevance scores Calculate opinion score for each document

9. 9 General Inquirer (Stone et al., 1966) OpinionFinder lexicon (Wiebe & Riloff, 2005) SentiWordNet (Esuli & Sebastiani, 2006) etc Lexicon-based Classification- based

10. 10 Calculate opinion score for each document General Inquirer (Stone et al., 1966) OpinionFinder lexicon (Wiebe & Riloff, 2005) SentiWordNet (Esuli & Sebastiani, 2006) etc Lexicon-based Classification- based

11. 11 A relevant blog post about “Munich”

12. 12 So, What is the problem?

13. 13 Also relevant to “Brokeback Mountain” and “Crash”

14. 14 Challenges 14

15. 15 Challenges query specific opinion score Final Ranking

16. 16 Topic Related Opinion Retrieval O: document expresses an opinion about the query

17. 17 Topic Related Opinion Retrieval RelevanceOpinion

18. 18 Topic Related Opinion Retrieval Proximity-based estimate

19. 19 A relevant blog post about “Munich”

20. 20 Non-Proximity Opinion Score Independence between q and o in d

21. 21 Non-Proximity Opinion Score lexicon

22. 22 Opinion Lexicon fortunate nice bad good poor wrong spoiled 1.0 0.96 0.95 0.98 0.89 0.88 0.93... EM algorithm SentiwordNet Amazon.com Review and Specification Corpus t p(o|t) Lee et al., KLE at TREC 2008

23. 23 Proximity-based Model Differentiating document’s positions

24. 24 Opinion Density of a document's Position is referring to How muchis opinionated

25. 25 Opinion Density of a document's Position lexicon kernel

26. 26 Opinion Density: P(o|i,d) nice heavy

27. 27 Opinion Density: P(o|i,d) nice heavy

28. 28 Propagated Opinion nice heavy

29. 29 Opinion Density: P(o|i,d) brokeback mountain munich brokeback

30. 30 Proximity-based Opinion Prob. Avg: Max:

31. 31 Different Kernels

32. 32 Different Kernels

33. 33 No statistically significant difference between kernels using the best parameter for each. Laplace kernel is less sensitive to the parameter Different Kernels

34. 34 Smoothed Proximity Model Capture Proximity at different ranges In docs where exact query term may be rare Opinion expressions refer to q indirectly via anaphoric expressions

35. 35 Relevance Retrieval Step

36. 36 A Common Approach to Opinion Retrieval Rank posts by relevance, select the highest ranking posts Combine the opinion and relevance scores Calculate opinion score for each document

37. 37 TREC Baselines Rank posts by relevance, select the highest ranking posts Combine the opinion and relevance scores Calculate opinion score for each document

38. 38 Topic Related Opinion Retrieval RelevanceOpinion

39. 39 Topic Related Opinion Retrieval estimate the relevance component

40. 40 Relevance Component

41. 41 Relevance Prob.

42. 42 Different ways of using relevance score TREC baseline 4 Relevance

43. 43 Different Relevant Opinion Scoring Method TREC baseline 4 Statistical significant over TREC relevance baselines

44. 44 Results over five standard TREC baselines Statistical significant over TREC relevance baselines Statistical significant over non-proximity opinion baseline

45. 45 per Topic Performance Analysis Carmax Yojimbo TomTom Picasa Mark Warner for President Iceland European Union Sheep and Wool Festival

46. 46 Results of the best runs on standard baseline 4 Statistical significant over TREC relevance baselines

47. 47 Conclusions A novel probabilistic model for blog opinion retrieval was proposed Proximity of opinion to query terms is a good indicator of their relatedness Laplace kernel was proposed and the effect of different kernels was studied Normalization can be important and the best normalization depends on the underlying relevance retrieval baseline

48. 48 Thanks! shima.gerani,mark.carma n,fabio.crestani @usi.ch