Subjectivity and Sentiment Analysis Jan Wiebe Department of
Subjectivity and Sentiment Analysis Jan Wiebe Department of Computer Science Intelligent Systems Program University of Pittsburgh
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.
What is Subjectivity? • The linguistic expression of somebody’s emotions, evaluations, beliefs, speculations, intentions, etc.
Subjectivity and Sentiment Analysis • Automatic extraction of people’s sentiments, opinions, etc. expressed in text (newspapers, blogs, etc)
Applications • • • Product review mining: Based on what people write in their reviews, what features of the Think. Pad T 43 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!
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.
Interpretation Dictionary definition meanings purely out of context Full contextual Interpretation of words in text
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text “The dream”
Interpretation Dictionary definition meanings purely out of context continuum NLP methods/resources building toward full interpretations Full contextual Interpretation of words in text “The dream”
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text “The dream” NLP methods/resources building toward full interpretations Today: 4 problems in subjectivity analysis along the continuum
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text
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? ")
Dictionary Definitions senses S O 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? ")
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
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.
“Subjectivity Sense Labeling” • Automatically classifying senses as subjective or objective, and classifying subjective senses by polarity
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text
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
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
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: <subject> was killed assassinated <dobj> murder of <np>
Learning Subjective Patterns • Method: IE-based techniques for learning extraction patterns
Patterns with Interesting Behavior PATTERN <subj> asked <subj> was expected from <np> FREQ 128 11 P(Subj | Pattern). 63 1. 0 45 5 . 42 1. 0 187 10 . 67. 90 <subj> talk of <np> <subj> is talk 28 10 5 . 71. 90 1. 0 <subj> is fact is <dobj> 38 12 1. 0 <subj> put end
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text
Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis
Prior versus Contextual Polarity • 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. ”
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
Features Binary features: • In subject [The human rights report] poses subj report det adj • In copular obj I am confident challenge mod The human rights det adj p a substantial … • In passive voice must be regarded Etcetera…
Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.
Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.
Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.
Contextual Polarity is Complex They have not succeeded, and will never succeed, in breaking the will of this valiant people.
Interpretation Dictionary definition meanings purely out of context continuum Full contextual Interpretation of words in text
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
Discourse-Level Opinion Interpretation Sentiment opinions include positive and negative evaluations, emotions, and judgments • • I like the LCD feature We must implement the LCD Arguing opinion include arguing for or against something, and arguing that something should or should not be done
Discourse-Level Opinion Interpretation • I like the LCD feature • We must implement the LCD targets: what the opinion is about
Discourse-Level Opinion Interpretation • I like the LCD feature • We must implement the LCD • I think the LCD is hot
Discourse-Level Opinion Interpretation • I like the LCD feature • We must implement the LCD • I think the LCD is hot Joint Interpretation of opinions in the discourse
Discourse-Level Opinion Interpretation • Goal: recognize opinion frames • Method: develop individual classifiers for their components, and then perform joint inference to improve performance
Manual Annotations I think people are happy because Chavez has fallen. direct subjective span: think source: <writer, I> attitude: attitude span: think type: positive arguing intensity: medium target: target span: people are happy because Chavez has fallen direct subjective span: are happy source: <writer, I, People> attitude: attitude span: are happy type: pos sentiment intensity: medium target: target span: Chavez has fallen inferred attitude span: are happy because Chavez has fallen type: neg sentiment intensity: medium target: target span: Chavez
<ppol=“neg”>condemn</ppol> Recognizing Context Polarity EMNLP 05 <ppol=“pos”>great</ppol> <ppol=“neg”>wicked</ppol> <> </> <> </> <cpol=“pos”>wicked </cpol> visuals <cpol=“neg”>loudly condemned</cpol> The building has been <subjectivity=“obj”> condemned </subjectivity> <> </> QA IE Opinion Tracking
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
Pointers • Please see http: //www. cs. pitt. edu/~wiebe – Publications – Opinion. Finder – Subjectivity lexicon – MPQA manually annotated corpus – Tutorials – Bibliography
(General) Subjectivity Types [Wilson 2008] Other (including cognitive) Note: similar ideas: polarity, semantic orientation, sentiment 73
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, …
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