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