Recognizing Intensity of Opinion Clauses
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This document summarizes research on recognizing the intensity of subjective opinion clauses, ranging from strongly negative to strongly positive. It discusses how opinion intensity can vary beyond being simply positive or negative. The researchers annotated over 10,000 sentences with subjective opinion clauses and their intensities. Several machine learning classifiers were trained on these annotations to predict the intensity of new clauses, with support vector machines performing best. Key factors included using bag-of-words features along with intensity or type groups of subjective clues.
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Recognizing Intensity of Opinion Clauses
- 1. Recognizing Strong and Weak Opinion Clauses Lucas Rizoli 2007-11-22 CPSC 503
- 2. Wilson, Wiebe, & Hwa, 2006 Recognizing Strong and Weak Opinion Clauses
- 3. Motivation Opinion is more than binary Intensity of expression How negative or positive? “I'm fond of you” vs. “I love you” Many applications Surveillance, marketing, retrieval...
- 4. Subjective expressions Surface-level phrases Express an internal state Opinion, emotion, sentiment, belief, etc. Can describe state directly “I love the taste of Zima”
- 5. Sometimes indirect “Rahul is so full of crap” Called an expressive subjective element Physical manifestation “Mayukh applauded the stripper” Called a private state action Vary in intensity neutral; low, medium, high, extreme
- 6. Annotation 3 annotators; ~10 000 sentences Who said it or expressed it “Sudeep said Sheelagh is a laugh” Objective and subjective “Sudeep said Sheelagh is a laugh”
- 7. Direct subjective element Source, span, intensity, expression intensity, implicit Expressive Subjective element Source, span, intensity Objective speech event element Source, span, implicit
- 8. Direct subjective element Source, span, intensity, expression intensity, implicit Expressive Subjective element Source, span, intensity Objective speech event element Source, span, implicit
- 9. Annotation notes Differing spans “Edwin is a silly canker-sore of a man.” “Edwin is a silly canker-sore of a man.” Considered any overlap as agreement Agreement on sub/objective elements 82%, 72% (Wiebe et al., 2005)
- 10. Agreement on intensity Direct & objective frames Intensity 0.79 (75%) Expression intensity 0.75 (62%) Subjective frames Intensity 0.46 (53%) Used Krippendorff's α
- 11. Subjective clues Prev lexicon and rules Verb classes, FrameNet, adjective sets, etc. Culled from literature & experience Syntax rules From dependency representation Part-of-speech in/sensitive
- 12. Clue groups Type grouping 29 Prev groups, by source 15 Syntax groups, by class and reliability Intensity grouping Grouped by P(intensity) Groups as classifier features
- 13. Classification Mean squared error, not accuracy Allows partially correct labeling Many classifiers Boosting, rules, SVM Clause-level intensity labels Highest intensity in a clause
- 19. Boosting Bag-of-words + Intensity StdError 0.99–1.211 Rules Intensity StdError 0.99–1.21 SVM Bag-of-words + Type or Intensity StdError 0.75–1.07
- 20. Easier to classify high-level clauses More information, training examples Boosting & Rules Off by 1 degree of intensity SVM Ordinal classifier Better by StdError, not Accuracy
- 21. Results SVM performs best Intensity and Type groups work best Syntax groups detrimental to all classifiers