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Subjectivity and Sentiment Analysis

Subjectivity and Sentiment Analysis. Jan Wiebe Department of Computer Science CERATOPS: Center for the Extraction and Summarization of Events and Opinions in Text University of Pittsburgh. What is Subjectivity?.

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Subjectivity and Sentiment Analysis

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  1. Subjectivity and Sentiment Analysis Jan Wiebe Department of Computer Science CERATOPS: Center for the Extraction and Summarization of Events and Opinions in Text University of Pittsburgh

  2. What is Subjectivity? • The linguisticexpression of somebody’s opinions, sentiments, emotions, evaluations, beliefs, speculations (private states) Wow, this is my 4th Olympus camera Staley declared it to be “one wicked song” Most voters believehe won‘t raise their taxes

  3. One Motivation • Automatic question answering…

  4. Fact-Based Question Answering Q: When is the first day of spring in 2007? Q: Does the US have a tax treaty with Cuba?

  5. Fact-Based Question Answering Q: When is the first day of spring in 2007? A:March 21 Q: Does the US have a tax treaty with Cuba? A:Thus, the U.S. has no tax treaties with nations like Iraq and Cuba.

  6. Opinion Question Answering Q: What is the international reaction to the reelection of Robert Mugabe as President of Zimbabwe?

  7. Opinion Question Answering Q: What is the international reaction to the reelection of Robert Mugabe as President of Zimbabwe? A: African observers generally approved of his victory while Western Governments stronglydenounced it.

  8. More Motivations • Product review mining:What features of the ThinkPad T43 do customers like and which do they dislike? • Review classification:Is a review positive or negative toward the movie? • Information Extraction: Is “killing two birds with one stone” a terrorist event? • Tracking sentiments toward topics over time:Is anger ratcheting up or cooling down? • Etcetera!

  9. <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  10. 1 <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  11. 2 <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  12. 3 <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  13. 4 <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  14. Outline • Subjectivity annotation scheme (MPQA) • Learning subjective expressions from unannotated texts • Contextual polarity of sentiment expressions • Word sense and subjectivity • Conclusions and pointers to related work

  15. 1 <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  16. Corpus Annotation Wiebe, Wilson, Cardie 2005 Wilson & Wiebe 2005 Somasundaran, Wiebe, Hoffmann, Litman 2006 Wilson 2007

  17. Outline for Section 1 • Overview • Frame types • Nested Sources • Extensions

  18. Overview • Fine-grained: expression level rather than sentence or document level • Annotate • expressions of opinions, sentiments, emotions, evaluations, speculations, … • material attributed to a source, but presented objectively

  19. Overview • Opinions, evaluations, emotions, speculations are private states • They areexpressed in language by subjective expressions Private state: state that is not open to objective observation or verification Quirk, Greenbaum, Leech, Svartvik (1985). A Comprehensive Grammar of the English Language.

  20. Overview • Focus on three ways private states are expressed in language • Direct subjective expressions • Expressive subjective elements • Objective speech events

  21. Direct Subjective Expressions • Direct mentions of private states The United Statesfearsa spill-over from the anti-terrorist campaign. • Private states expressed in speech events “We foresaw electoral fraud but not daylight robbery,” Tsvangirai said.

  22. Expressive Subjective Elements Banfield 1982 • “We foresaw electoral fraud but not daylight robbery,” Tsvangirai said • The part of the US human rights report about China is full of absurdities and fabrications

  23. Objective Speech Events • Material attributed to a source, but presented as objective fact (without evaluation…) The government, itadded, has amended the Pakistan Citizenship Act 10 of 1951 to enable women of Pakistani descent to claim Pakistani nationality for their children born to foreign husbands.

  24. Nested Sources “The report is full of absurdities,’’ Xirao-Nima said the next day.

  25. “The report is full of absurdities,’’ Xirao-Nima said the next day. Nested Sources (Writer)

  26. “The report is full of absurdities,’’ Xirao-Nima said the next day. Nested Sources (Writer, Xirao-Nima)

  27. “The report is full of absurdities,’’ Xirao-Nima said the next day. Nested Sources (Writer Xirao-Nima) (Writer Xirao-Nima)

  28. “The report is full of absurdities,’’ Xirao-Nima said the next day. Nested Sources (Writer) (Writer Xirao-Nima) (Writer Xirao-Nima)

  29. “The report is full of absurdities,” Xirao-Nima said the next day. Objective speech event anchor:the entire sentence source: <writer> implicit: true Direct subjective anchor:said source: <writer, Xirao-Nima> intensity: high expression intensity: neutral attitude type:negative target:report Expressive subjective element anchor:full of absurdities source: <writer, Xirao-Nima> intensity: high attitude type: negative

  30. “The report is full of absurdities,” Xirao-Nima said the next day. Objective speech event anchor:the entire sentence source: <writer> implicit: true Direct subjective anchor:said source: <writer, Xirao-Nima> intensity: high expression intensity: neutral attitude type:negative target:report Expressive subjective element anchor:full of absurdities source: <writer, Xirao-Nima> intensity: high attitude type: negative

  31. “The report is full of absurdities,” Xirao-Nima said the next day. Objective speech event anchor:the entire sentence source: <writer> implicit: true Direct subjective anchor:said source: <writer, Xirao-Nima> intensity: high expression intensity: neutral attitude type:negative target:report Expressive subjective element anchor:full of absurdities source: <writer, Xirao-Nima> intensity: high attitude type: negative

  32. “The report is full of absurdities,” Xirao-Nima said the next day. Objective speech event anchor:the entire sentence source: <writer> implicit: true Direct subjective anchor:said source: <writer, Xirao-Nima> intensity: high expression intensity: neutral attitude type:negative target:report Expressive subjective element anchor:full of absurdities source: <writer, Xirao-Nima> intensity: high attitude type: negative

  33. “The report is full of absurdities,” Xirao-Nima said the next day. Objective speech event anchor:the entire sentence source: <writer> implicit: true Direct subjective anchor:said source: <writer, Xirao-Nima> intensity: high expression intensity: neutral attitude type:negative target:report Expressive subjective element anchor:full of absurdities source: <writer, Xirao-Nima> intensity: high attitude type: negative

  34. “The report is full of absurdities,” Xirao-Nima said the next day. Objective speech event anchor:the entire sentence source: <writer> implicit: true Direct subjective anchor:said source: <writer, Xirao-Nima> intensity: high expression intensity: neutral attitude type:negative target:report Expressive subjective element anchor:full of absurdities source: <writer, Xirao-Nima> intensity: high attitude type: negative

  35. “The US fears a spill-over’’, said Xirao-Nima, a professor of foreign affairs at the Central University for Nationalities.

  36. (Writer) “The US fears a spill-over’’, said Xirao-Nima, a professor of foreign affairs at the Central University for Nationalities.

  37. (writer, Xirao-Nima) “The US fears a spill-over’’, said Xirao-Nima, a professor of foreign affairs at the Central University for Nationalities.

  38. (writer, Xirao-Nima, US) “The US fears a spill-over’’, said Xirao-Nima, a professor of foreign affairs at the Central University for Nationalities.

  39. (Writer) (writer, Xirao-Nima, US) (writer, Xirao-Nima) “The US fears a spill-over’’, said Xirao-Nima, a professor of foreign affairs at the Central University for Nationalities.

  40. “The US fears a spill-over’’, said Xirao-Nima, a professor of foreign affairs at the Central University for Nationalities. Objective speech event anchor:the entire sentence source: <writer> implicit: true Objective speech event anchor:said source: <writer, Xirao-Nima> Direct subjective anchor:fears source: <writer, Xirao-Nima, US> intensity: medium expression intensity:medium attitude type:negative target:spill-over

  41. Corpus • @ www.cs.pitt.edu/mpqa • English language versions of articles from the world press (187 news sources) • Also includes contextual polarity annotations • Themes of the instructions: • No rules about how particular words should be annotated. • Don’t take expressions out of contextand think about what they could mean, but judge them as they are used in that sentence.

  42. ExtensionsWilson 2007

  43. ExtensionsWilson 2007 I think people are happy because Chavez has fallen. direct subjective span: think source: <writer, I> attitude: direct subjective span: are happy source: <writer, I, People> attitude: inferred attitude span: are happy because Chavez has fallen type: neg sentiment intensity: medium target: attitude span: think type: positive arguing intensity: medium target: attitude span: are happy type: pos sentiment intensity: medium target: target span: people are happy because Chavez has fallen target span: Chavez has fallen target span: Chavez

  44. Outline • Subjectivity annotation scheme (MPQA) • Learning subjective expressions from unannotated texts • Contextual polarity of sentiment expressions • Word sense and subjectivity • Conclusions and pointers to related work

  45. 2 <ppol=“neg”>condemn</ppol> <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </><> </> <> </><> </> <> </><> </> <> </><> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> QA Review Mining Opinion Tracking The buildinghas been <subjectivity=“obj”> condemned </subjectivity>

  46. Learning subjective nouns with extraction pattern bootstrapping Automatically generating training data with high-precision classifiers Learning subjective and objective expressions (not simply words or n-grams) Riloff, Wiebe, Wilson 2003; Riloff & Wiebe 2003; Wiebe & Riloff 2005; Riloff, Patwardhan, Wiebe 2006. Outline for Section 2

  47. Learning subjective nouns with extraction pattern bootstrapping Automatically generating training data with high-precision classifiers Learning subjective and objective expressions Outline for Section 2

  48. Information extraction (IE) systems identify facts related to a domain of interest. Extraction patterns are lexico-syntactic expressions that identify the role of an object. For example: Information Extraction <subject> was killed assassinated <dobj> murder of <np>

  49. Learning Subjective Nouns • Goal: to learn subjective nouns from unannotated text • Method: applying IE-based bootstrapping algorithms that were designed to learn semantic categories • Hypothesis: extraction patterns can identify subjective contexts that co-occur with subjective nouns Example: “expressed <dobj>” concern, hope, support

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