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Opinion Integration and Summarization

ChengXiang (“Cheng”) Zhai Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology Department of Statistics University of Illinois, Urbana-Champaign. Opinion Integration and Summarization.

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Opinion Integration and Summarization

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  1. ChengXiang (“Cheng”) Zhai • Department of Computer Science • Graduate School of Library & Information Science • Institute for Genomic Biology • Department of Statistics • University of Illinois, Urbana-Champaign Opinion Integration and Summarization Joint work with Qiaozhu Mei (U. Mich), Yue Lu (UIUC), Hyun Duk Kim (UIUC)

  2. Effective Information Sharing Decision=? Summary ? Information Need Information Nuggets Information Sharing Network

  3. Opinion Integration & Summarization Information Nuggets Opinion Integration Sentiment Analysis Contradictory Opinion Analysis [Lu & Zhai 08] [Kim & Zhai 09] [Mei et al. 07]

  4. 1. Opinion Integration [Lu & Zhai WWW 08] Output Input Similar Supplementary Design Battery Price Topic: iPod DesignBatteryPrice.. Expert review with aspects Scattered opinions Integrated Summary

  5. Sample Integration Result: iPhone

  6. Extra Aspects from Blogs

  7. Blog Coverage of Review Aspects 7

  8. 2. Multi-Faceted Sentiment Summary [Mei et al. WWW 07] query=“Da Vinci Code”

  9. Separate Theme Sentiment Dynamics “religious belief” “book”

  10. Choose a facet (subtopic) i battery battery 0.3 life 0.2.. F Facet 1 love P N nano 0.1release 0.05screen 0.02 .. Facet 2 1 F 2 P N … apple 0.2microsoft 0.1compete 0.05 .. F hate k Facet k P N B Is 0.05the 0.04a 0.03 .. the Background B love 0.2awesome 0.05good 0.01 .. suck 0.07hate 0.06stupid 0.02 .. N P Probabilistic Topic-Sentiment Model …

  11. 3. Summarization of Contradictory Opinions [Kim & Zhai CIKM 09] How can we help analysts digest and interpret contradictory opinioons?

  12. Contrastive Opinion Summarization X Y x1 y1 x2 y2 x3 y3 x4 y4 … x5 ym … xn

  13. Contrastive Opinion Summarization X Y Contrastive Opinion Summary x1 y1 x2 y2 u1 v1 x3 y3 u2 v2 … … x4 y4 uk vk … x5 ym … xn

  14. Problem Formulation Representativeness X Y x1 U V y1 x2 y2 u1 v1 x3 y3 u2 v2 … … x4 y4 uk vk … x5 ym … Contrastiveness xn

  15. Problem Formulation Representativeness X Y x1 U V y1 x2 y2 u1 v1 x3 y3 u2 v2 … … x4 y4 uk vk … x5 ym … Contrastiveness xn

  16. Summarization as Optimization 1. Define an appropriate content similarity function Ф 2. Define an appropriate contrastive similarity function ψ 3. Solve the optimization problem efficiently.

  17. Sample Results

  18. Sample Result Different polarities of opinions made from different perspectives.

  19. Sample Result Positive vs. negative Not much disagreement

  20. Sample Result Judgments revealing detailed conditions

  21. Summary Information Nuggets Opinion Integration Sentiment Analysis Contradictory Opinion Analysis [Lu & Zhai 08] [Kim & Zhai 09] [Mei et al. 07]

  22. Future Plan • Incorporate trustworthiness of sources in opinion integration • Analyze opinions in context and discover topic communities • Suggest opportunities for information sharing • “Soft” policy of information sharing

  23. References Opinion integration: [WWW 08] Y. Lu and C. Zhai. Opinion integration through semi-supervised topic modeling. In WWW ’08: Proceeding of the 17th international conference on World Wide Web, pages 121–130, New York, NY, USA, 2008. Sentiment analysis: [WWW 07] Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai. Topic sentiment mixture: modeling facets and opinions in weblogs. In WWW ’07: Proceedings of the 16th international conference on World Wide Web, pages 171–180, New York, NY, USA, 2007. Contradictory opinion summarization: [CIKM 09] H. Kim, C. Zhai, Generating Comparative Summaries ofContradictory Opinions in Text, In Proceedings of CIKM 2009, to appear.

  24. Questions/Comments/Suggestions? http://apps.facebook.com/news_letters/

  25. 1. Content Similarity Function : term similarity function has Sentence 1 It short battery time great Sentence 2 The battery life Isn’t

  26. 1. Content Similarity Function : term similarity function has Sentence 1 It short battery time great Sentence 2 The battery life Isn’t

  27. 1. Content Similarity Function : term similarity function has Sentence 1 It short battery time great Sentence 2 The battery life Isn’t

  28. 2. Contrastive Similarity Function : term similarity function has Sentence 1 It short battery time great Sentence 2 The battery life Isn’t

  29. 3. Approximation Algorithms X Y x1 y1 x2 y2 x3 y3 x4 y4 … Combinations x5 ym … xn

  30. Strategy 1: Representativeness-First X Y x1 y1 x4 y2 x3 y4 x2 y6 … x7 ym … Find k cluster, xn

  31. Strategy 1: Representativeness-First X Y Find k cluster,  Find contrastive pairs x1 U V y1 x4 y2 u1 v1 x3 y4 u2 v2 … … x2 y6 uk vk … x7 ym … xn

  32. Strategy 2: Contrastiveness-First X Y x1 y1 x2 y2 x3 y3 x4 y4 … x5 ym … xn

  33. Strategy 2: Contrastiveness-First X Y x1 y2 x1 y1 xn y4 x2 y2 x7 y1 x3 y3 x3 ym x4 y4 … x8 y5 x5 … … ym … Find contrastive pair, xn

  34. Strategy 2: Contrastiveness-First X Y x1 y2 x1 y1 xn y4 x2 y2 k x7 y1 x3 y3 x3 ym x4 y4 … x8 y5 x5 … … ym … Find contrastive pair,  Select representative pairs xn

  35. Rep-First Outperforms Contrast-First

  36. Contrastive Similarity Heuristic Works Removing sentiment words is beneficial

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