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 – 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

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 Next slide