tailieunhanh - Báo cáo khoa học: "Structured Models for Fine-to-Coarse Sentiment Analysis"

In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequence classification techniques using constrained Viterbi to ensure consistent solutions. The primary advantage of such a model is that it allows classification decisions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classification error relative to models trained in isolation. . | Structured Models for Fine-to-Coarse Sentiment Analysis Ryan McDonald Kerry Hannan Tyler Neylon Mike Wells Jeff Reynar Google Inc. 76 Ninth Avenue New York NY 10011 Contact email ryanmcd@ Abstract In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Inference in the model is based on standard sequence classification techniques using constrained Viterbi to ensure consistent solutions. The primary advantage of such a model is that it allows classification decisions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classification error relative to models trained in isolation. 1 Introduction Extracting sentiment from text is a challenging problem with applications throughout Natural Language Processing and Information Retrieval. Previous work on sentiment analysis has covered a wide range of tasks including polarity classification Pang et al. 2002 Turney 2002 opinion extraction Pang and Lee 2004 and opinion source assignment Choi et al. 2005 Choi et al. 2006 . Furthermore these systems have tackled the problem at different levels of granularity from the document level Pang et al. 2002 sentence level Pang and Lee 2004 Mao and Lebanon 2006 phrase level Turney 2002 Choi et al. 2005 as well as the speaker level in debates Thomas et al. 2006 . The ability to classify sentiment on multiple levels is important since different applications have different needs. For example a summarization system for product 432 reviews might require polarity classification at the sentence or phrase level a question answering system would most likely require the sentiment of paragraphs and a system that determines which articles from an online news source are editorial in nature would require a document level analysis. This work focuses on models that jointly classify sentiment on multiple levels of granularity. Consider the following .