Isotonic Conditional Random Fields and Local Sentiment Flow.pdfVIP

Isotonic Conditional Random Fields and Local Sentiment Flow Yi Mao Guy Lebanon School of Elec. and Computer Engineering Department of Statistics, and Purdue University - West Lafayette, IN School of Elec. and Computer Engineering ymao@ Purdue University - West Lafayette, IN lebanon@ Abstract 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 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 Introduction The World Wide Web and other textual databases provide a convenient platform for exchanging opinions. Many documents, such as reviews and blogs, are written with the purpose of conveying a particular opinion or sentiment. Other documents may not be written with the purpose of conveying an opinion, but nevertheless they contain one. Opinions, or sentiments, may be considered in several ways, the simplest of which is varying from positive opinion, through neutral, to negative opinion. Most of the research in information retrieval has focused on predicting the topic of a document, or its relevance with respect to a query. Predicting the document’s sentiment would allow matching the sentiment, as well as the topic, with the use