Isotonic Conditional Random Fields and Local Sentiment Flow

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Yi Mao, Guy Lebanon


We examine the problem of predicting local sentiment flow in documents, and its application to several areas of text analysis. Formally, the problem is stated as predicting an ordinal sequence based on a sequence of word sets. In the spirit of isotonic regression, we develop a variant of conditional random fields that is well suited to handle this problem. Using the Mobius transform, we express the model as a simple convex optimization problem. Experiments demonstrate the model and its applications to sentiment prediction, style analysis, and text summarization.