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We study the problem of agreement and disagreement detection in onlinediscussions. An isotonic Conditional Random Fields (isotonic CRF) basedsequential model is proposed to make predictions on sentence- or segment-level.We automatically construct a socially-tuned lexicon that is bootstrapped fromexisting general-purpose sentiment lexicons to further improve the performance.We evaluate our agreement and disagreement tagging model on two disparateonline discussion corpora -- Wikipedia Talk pages and online debates. Our modelis shown to outperform the state-of-the-art approaches in both datasets. Forexample, the isotonic CRF model achieves F1 scores of 0.74 and 0.67 foragreement and disagreement detection, when a linear chain CRF obtains 0.58 and0.56 for the discussions on Wikipedia Talk pages.

Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon