recognizing stances in ideological online debates
TRANSCRIPT
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
• 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
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
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
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
Experiments
• SVM• See table 4
How can you say such things? Recognizing Disagreement in Informal Political Arguement
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
Machine Learning Setup
• Classifiers– Naïve Bayes– JRip
• Feature Extraction
Feature Extraction
• Unigrams,Bigrams• MetaPost info• Discourse Markers (Cue words,initial uni/bigrams)• Repeated Punctuation• LIWC (linguistic inquiry word count tool)• Dependency and generalized Dependency• Opinion Dependencies• Annotations• See table 2 and table 3
Experiments and results
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