recognizing stances in ideological online debates

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Recognizing Stances in Ideological Online Debates

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Page 1: Recognizing Stances in Ideological Online Debates

Recognizing Stances in Ideological Online Debates

Page 2: 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

Page 3: Recognizing Stances in Ideological Online Debates
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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

Page 5: Recognizing Stances in Ideological Online Debates
Page 6: Recognizing Stances in Ideological Online Debates

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

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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

Page 8: Recognizing Stances in Ideological Online Debates

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

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Experiments

• SVM• See table 4

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

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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

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Machine Learning Setup

• Classifiers– Naïve Bayes– JRip

• Feature Extraction

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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

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Experiments and results

• Next slide

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