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Recognizing Stances in Ideological Online Debates

Recognizing Stances in Ideological Online Debates. Introduction. Dataset: MPQA Corpus Totally 6 ideological and political domains 2 for development of classifier 4 for experiment and analyses Create features opinion-target features See table 1. Constructing an arguing lexicon.

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Recognizing Stances in Ideological Online Debates

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  1. Recognizing Stances in Ideological Online Debates

  2. Introduction • Dataset: MPQA Corpus • Totally 6 ideological and political domains • 2 for development of classifier • 4 for experiment and analyses • Create features opinion-target features • See table 1

  3. Constructing an arguing lexicon • Government is a disease pretending to be its own cure. [side: against healthcare] • I most certainly believe that there are some ESSENTIAL, IMPORTANT things that the government has or must do [side: for healthcare] • Oh, the answer is GREEDY insurance companies that buy your Rep & Senator. [side: for healthcare] • See table 2

  4. Constructing an arguing lexicon • (Before constructing an arguing lexicon) • Generate a candidate Set • Remove the candidates that are present in the sentiment lexicon from (Wilson et al., 2005) (as these are already accounted for in previous research). • For each candidate in the candidate Set, find the likelihood

  5. Contd • P (positive arguing|candidate) = #candidate is in a positive arguing span/#candidate is in the corpus • P (negative arguing|candidate) = #candidate is in a negative arguing span/#candidate is in the corpus • Make lexicon entry with probabilities

  6. Features • Arguing Lexicon features • Tri/bi/unigram arguing expression(in that order) • Modal Verb features • Must,should,… • Syntactic rules • Eg. They must be available to all people ( SVO ) • Sentiment-based features • Use sentiment lexicon (Wilson & Wiebe) • Determine sentiment polarity using vote and flip algorithm

  7. Experiments • SVM • See table 4

  8. How can you say such things? Recognizing Disagreement in Informal Political Arguement

  9. Data and Corpus analysis • Data and Corpus analysis • Agree/Disagee • Fact/Emotion • Attack/Insult • Sarcasm • Nice/Nasty • See table 1 • Discourse Markers • Eg. actually, and, because, but, I believe, I know, I see, I think, just, no, oh, really, so, well, yes, you know, you mean

  10. Machine Learning Setup • Classifiers • Naïve Bayes • JRip • Feature Extraction

  11. Feature Extraction • Unigrams,Bigrams • MetaPost info • Discourse Markers (Cue words,initialuni/bigrams) • Repeated Punctuation • LIWC (linguistic inquiry word count tool) • Dependency and generalized Dependency • Opinion Dependencies • Annotations • See table 2 and table 3

  12. Experiments and results • Next slide

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