Isotonic Conditional Random Fields and Local Sentiment Flow
Yi Mao, Guy Lebanon · The MIT Press eBooks · 2007
We examine the problem of predicting local sentiment ow 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 elds that is well-suited to handle this problem. Using the M¤obius 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. 1