1. Subjectivity and Sentiment Analysis Jan Wiebe Department of Computer Science Intelligent Systems Program University of Pittsburgh

2. Main Collaborators in the Work Described Today Claire Cardie, Cornell University Rada Mihalcea, University of North Texas Ellen Riloff, University of Utah Swapna Somasundaran, U. Pittsburgh Theresa Wilson, University of Edinburgh

3. Burgeoning Field Quite a large problem space Several terms reflecting varying goals and models –Sentiment Analysis –Opinion Mining –Opinion Extraction –Subjectivity Analysis –Appraisal Analysis –Affect Sensing –Emotion Detection –Identifying Perspective –Etc.

4. What is Subjectivity? The linguistic expression of somebody’s emotions, evaluations, beliefs, speculations, intentions, etc. Here, sentiment is a type of subjectivity (positive and negative emotions and evaluations )

5. What is Subjectivity? The linguistic expression of somebody’s emotions, evaluations, beliefs, speculations, intentions, etc.

6. Subjectivity and Sentiment Analysis Automatic extraction of people’s sentiments, opinions, etc. expressed in text (newspapers, blogs, etc)

7. Applications Product review mining: Based on what people write in their reviews, what features of the ThinkPad T43 do they like and which do they dislike? Review classification: Is a review positive or negative toward the movie? Tracking sentiments toward topics over time: Based on sentiments expressed in text, is anger ratcheting up or cooling down? Prediction (election outcomes, market trends): Based on opinions expressed in text, will Clinton or Obama win? Etcetera!

8. Focus Subjective language is highly ambiguous Simple keyword approaches are severely limited Our focus is linguistic disambiguation; how should language be interpreted? –Is it subjective in the first place? If so, is it positive or negative? How intense is it? Etc.

9. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text

10. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum

11. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum “The dream”

12. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum “The dream” NLP methods/resources building toward full interpretations

13. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum “The dream” NLP methods/resources building toward full interpretations Today: 4 problems in subjectivity analysis along the continuum

14. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum

15. Dictionary Definitions senses Interest, involvement -- (a sense of concern with and curiosity about someone or something; "an interest in music") Interest -- (a fixed charge for borrowing money; usually a percentage of the amount borrowed; "how much interest do you pay on your mortgage?")

16. Dictionary Definitions senses Interest, involvement -- (a sense of concern with and curiosity about someone or something; "an interest in music") Interest -- (a fixed charge for borrowing money; usually a percentage of the amount borrowed; "how much interest do you pay on your mortgage?") S O

17. Senses Most approaches to subjectivity and sentiment analysis exploit subjectivity lexicons, which are lists of keywords that have been gathered together because they have subjective usages Even in subjectivity lexicons, many senses of the keywords are objective -- ~50% in our study! So, many appearances of keywords in texts are false hits

18. Senses His alarm grew as the election returns came in. He forgot to set his alarm. His trust grew as the candidate spoke. His trust grew as interest rates increased.

19. “Subjectivity Sense Labeling” Automatically classifying senses as subjective or objective, and classifying subjective senses by polarity

20. WSD using Subjectivity Tagging The notes do not pay interest. He spins a riveting plot which grabs and holds the reader’s interest. Sense 4 “a sense of concern with and curiosity about someone or something” S Sense 1 “a fixed charge for borrowing money” O WSD System Sense 4 Sense 1? Sense 1 Sense 4?

21. Sense 4 “a sense of concern with and curiosity about someone or something” S Sense 1 “a fixed charge for borrowing money” O WSD using Subjectivity Tagging The notes do not pay interest. He spins a riveting plot which grabs and holds the reader’s interest. WSD System Sense 4 Sense 1? Sense 1 Sense 4? Subjectivity Classifier S O

22. Sense 4 “a sense of concern with and curiosity about someone or something” S Sense 1 “a fixed charge for borrowing money” O WSD using Subjectivity Tagging The notes do not pay interest. He spins a riveting plot which grabs and holds the reader’s interest. WSD System Sense 4 Sense 1? Sense 1 Sense 4? Subjectivity Classifier S O

23. Subjectivity Classifier Subjectivity Tagging using WSD The notes do not pay interest. He spins a riveting plot which grabs and holds the reader’s interest. O S? S O?

24. Subjectivity Classifier S Sense 4 “a sense of concern with and curiosity about someone or something” O Sense 1 “a fixed charge for borrowing money” Subjectivity Tagging using WSD The notes do not pay interest. He spins a riveting plot which grabs and holds the reader’s interest. WSD System Sense 4 Sense 1 O S? S O?

25. Subjectivity Classifier S Sense 4 “a sense of concern with and curiosity about someone or something” O Sense 1 “a fixed charge for borrowing money” Subjectivity Tagging using WSD The notes do not pay interest He spins a riveting plot which grabs and holds the reader’s interest. WSD System Sense 4 Sense 1 O S? S O?

26. Methods for Subjectivity Sense Labeling Corpus based Lexicon based (exploiting knowledge in WordNet) Integration

27. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum

28. Learning Subjective Language from Corpora There is a seemingly endless variety of subjective expressions, i.e., expressions that may be used to express opinions and sentiments Many do not correspond to dictionary definitions Subjective language varies among different types of corpora

29. Learning Subjective Language from Corpora Goal: create subjective language learners that do not require manually annotated texts as input Learners may be applied to –large text collections to generate more expansive dictionaries –domain specific corpora with specialized vocabularies Methods: weakly supervised information extraction methods

30. Information Extraction 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: was killed assassinated murder of

31. Learning Subjective Language Use IE techniques to learn subjective nouns Use IE techniques to learn subjective patterns

32. Learning Subjective Nouns Hypothesis: extraction patterns can identify subjective contexts that co-occur with subjective nouns Example: “expressed ” concern, hope, support

33. Learning Subjective Nouns Method: IE-based bootstrapping algorithms designed to learn semantic categories

34. Extraction Examples expressed condolences, hope, grief, views, worries indicative of compromise, desire, thinking inject vitality, hatred reaffirmed resolve, position, commitment voiced outrage, support, skepticism, opposition, gratitude, indignation show of support, strength, goodwill, solidarity was sharedanxiety, view, niceties, feeling

35. Meta-Bootstrapping [Riloff & Jones 99] Unannotated Texts Best Extraction Pattern Extractions (Nouns) Ex: hope, grief, joy, concern, worries Ex: expressed Ex: happiness, relief, condolences

36. Subjective Seed Words cowardiceembarrassment hatred outrage crapfool hell slander delightgloom hypocrisy sigh disdaingrievance love twit dismayhappiness nonsense virtue

37. Examples of Learned Nouns anguish exploitation pariah antagonism evil repudiation apologist fallacies revenge atrocities genius rogue barbarian goodwill sanctimonious belligerence humiliationscum bully ill-treatment smokescreen condemnation injustice sympathy denunciation innuendo tyranny devil insinuation venom diatribe liar exaggeration mockery

38. Learning Subjective Patterns Extraction patterns can represent linguistic expressions that are not fixed word sequences. drove [NP] up the wall - drove him up the wall - drove George Bush up the wall - drove George Herbert Walker Bush up the wall step on [modifiers] toes - step on her toes - step on the mayor’s toes - step on the newly elected mayor’s toes

39. Learning Subjective Patterns Method: IE-based techniques for learning extraction patterns

40. Learning Subjective Patterns Method: IE-based techniques for learning extraction patterns themselves

41. Patterns with Interesting Behavior PATTERNFREQP(Subj | Pattern) asked 128.63 was asked 11 1.0 was expected 45.42 was expected from 5 1.0 talk 28.71 talk of 10.90 is talk 5 1.0 put 187.67 put end 10.90 is fact 38 1.0 fact is 12 1.0

42. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum

43. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis

44. Several subjectivity lexicons include polarity information beautiful  positive horrid  negative In context, words often appear in phrases with the opposite polarity “Cheers to Timothy Whitfield for the wonderfully horrid visuals.” Prior versus Contextual Polarity

45. Recognizing Contextual Polarity Goal: given a phrase containing a word from the lexicon, is it subjective? If so, is it positive or negative? Method: machine learning with a variety of features

46. Features Binary features: In subject [The human rights report] poses In copular I am confident In passive voice must be regarded Thehumanrights report poses asubstantial challenge … det adj mod adj det subjobj p Etcetera…

47. Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.

48. Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.

49. Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.

50. Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.

51. Features Contextual valence shifters General polarity shifter have few risks/rewards Negative polarity shifter lack of understanding Positive polarity shifter abate the damage

52. Features Structural features Binary features: In subject [The human rights report] poses In copular I am confident In passive voice must be regarded Thehumanrights report poses asubstantial challenge … det adj mod adj det subjobj p

53. Features Modification features Modifies polarity substantial: negative Modified by polarity challenge: positive substantial (pos) challenge (neg)

54. Features Negation features Binary features: Negated For example: –not good –does not look very good  not only good but amazing Negated subject No politically prudent Israeli could support either of them.

55. Features Etc.

56. Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text continuum

57. Discourse-Level Opinion Interpretation Interpretations involving multiple sentences within the discourse Opinion Frames are composed of 2 opinions and the relation between their targets (what they are opinions of) Larger structures emerge from interdependent frames

58. I like the LCD feature We must implement the LCD Discourse-Level Opinion Interpretation Sentiment opinions include positive and negative evaluations, emotions, and judgments Arguing opinion include arguing for or against something, and arguing that something should or should not be done

59. I like the LCD feature We must implement the LCD Discourse-Level Opinion Interpretation targets: what the opinion is about

60. I like the LCD feature We must implement the LCD I think the LCD is hot Discourse-Level Opinion Interpretation

61. I like the LCD feature We must implement the LCD I think the LCD is hot Discourse-Level Opinion Interpretation Joint Interpretation of opinions in the discourse

62. Discourse-Level Opinion Interpretation Goal: recognize opinion frames Method: develop individual classifiers for their components, and then perform joint inference to improve performance

63. D::... this kind of rubbery material, it’s a bit more bouncy, like you said they get chucked around a lot. A bit more durable and that can also be ergonomic and it kind of feels a bit different from all the other remote controls. S? SP Opinion Frames: Interdependent Interpretation SP

64. D::... this kind of rubbery material, it’s a bit more bouncy, like you said they get chucked around a lot. A bit more durable and that can also be ergonomic and it kind of feels a bit different from all the other remote controls. same S? SP Opinion Frames: Interdependent Interpretation

65. D::... this kind of rubbery material, it’s a bit more bouncy, like you said they get chucked around a lot. A bit more durable and that can also be ergonomic and it kind of feels a bit different from all the other remote controls. same S? SP reinforcing SP reinforcing Opinion Frames: Interdependent Interpretation

66. D::... this kind of rubbery material, it’s a bit more bouncy, like you said they get chucked around a lot. A bit more durable and that can also be ergonomic and it kind of feels a bit different from all the other remote controls. same S? SP reinforcing SP reinforcing SPSPsame, SNSNsame, APAPsame, ANANsame, SPAPsame, APSPsame, SNANsame, ANSNsame, SPSNalt, SNSPalt, APANalt, ANAPalt, SPANalt, SNAPalt, APSNalt, ANSPalt SPSNsame, SNSPsame, APANsame, ANAPsame, SPANsame, APSNsame, SNAPsame, ANSPsame, SPSPalt, SNSNalt, APAPalt, ANANalt, SPAPalt, SNANalt, APSPalt, ANSNalt reinforcing non-reinforcing Opinion Frames: Interdependent Interpretation

67. D::... this kind of rubbery material, it’s a bit more bouncy, like you said they get chucked around a lot. A bit more durable and that can also be ergonomic and it kind of feels a bit different from all the other remote controls. same S? SP reinforcing SP reinforcing SPSPsame, SNSNsame, APAPsame, ANANsame, SPAPsame, APSPsame, SNANsame, ANSNsame, SPSNalt, SNSPalt, APANalt, ANAPalt, SPANalt, SNAPalt, APSNalt, ANSPalt SPSNsame, SNSPsame, APANsame, ANAPsame, SPANsame, APSNsame, SNAPsame, ANSPsame, SPSPalt, SNSNalt, APAPalt, ANANalt, SPAPalt, SNANalt, APSPalt, ANSNalt reinforcing non-reinforcing Opinion Frames: Interdependent Interpretation

68. D::... this kind of rubbery material, it’s a bit more bouncy, like you said they get chucked around a lot. A bit more durable and that can also be ergonomic and it kind of feels a bit different from all the other remote controls. same SP reinforcing SP reinforcing SPSPsame, SNSNsame, APAPsame, ANANsame, SPAPsame, APSPsame, SNANsame, ANSNsame, SPSNalt, SNSPalt, APANalt, ANAPalt, SPANalt, SNAPalt, APSNalt, ANSPalt SPSNsame, SNSPsame, APANsame, ANAPsame, SPANsame, APSNsame, SNAPsame, Etc, SPSPalt, SNSNalt, APAPalt, ANANalt, SPAPalt, SNANalt, APSPalt, ANSNalt reinforcing non-reinforcing SP Opinion Frames: Interdependent Interpretation

69. Manual Annotations I think people are happy because Chavez has fallen. direct subjective span: are happy source: attitude: inferred attitude span: are happy because Chavez has fallen type: neg sentiment intensity: medium target: target span: Chavez has fallen target span: Chavez attitude span: are happy type: pos sentiment intensity: medium target: direct subjective span: think source: attitude: attitude span: think type: positive arguing intensity: medium target: target span: people are happy because Chavez has fallen

70. wicked visuals loudly condemned The building has been condemned QA IE Opinion Tracking condemn great wicked <> </><> </> <> </> <> </> Recognizing Context Polarity EMNLP05

71. Other Recent Projects Learning Multilingual Subjective Language via Cross-Lingual Projections “Universal representation” of subjectivity clues –Single words –N-grams –Word senses –Lexico-syntactic patterns –Broken into definitional and (standoff) attributional components Exploiting subjectivity analysis to improve Information extraction and automatic question answering systems

72. Pointers Please see http://www.cs.pitt.edu/~wiebehttp://www.cs.pitt.edu/~wiebe –Publications –OpinionFinder –Subjectivity lexicon –MPQA manually annotated corpus –Tutorials –Bibliography

73. 73 (General) Subjectivity Types [Wilson 2008] Other (including cognitive) Note: similar ideas: polarity, semantic orientation, sentiment

74. Acknowledgements CERATOPS Center for the Extraction and Summarization of Events and Opinions in Text –Pittsburgh: Paul Hoffmann, Josef Ruppenhofer, Swapna Somasundaran, Theresa Wilson –Cornell: Claire Cardie, Eric Breck, Yejin Choi, Ves Stoyanov –Utah: Ellen Riloff, Sidd Patwardhan, Bill Phillips UNT: Rada Mihalcea, Carmen Banea NLP@Pitt: Wendy Chapman, Rebecca Hwa, Diane Litman, …