Structured Models for Fine-to-Coarse Sentiment Analysis

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 432–439, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics 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@google.com Abstract In this paper we investigate a structured model for jointly classifying the sentiment of text at varying levels of granularity. Infer- ence in the model is based on standard se- quence classification techniques using con- strained Viterbi to ensure consistent solu- tions. The primary advantage of such a model is that it allows classification deci- sions from one level in the text to influence decisions at another. Experiments show that this method can significantly reduce classifi- cation error relative to models trained in iso- lation. 1 Introduction Extracting sentiment from text is a challenging prob- lem 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 differ- ent levels of granularity, from the document level (Pang et al., 2002), sentence level (Pang and Lee, 2004; Mao and Lebanon, 2006), phrase level (Tur- ney, 2002; Choi et al., 2005), as well as the speaker level in debates (Thomas et al., 2006). The abil- ity to classify sentiment on multiple levels is impor- tant since different applications have different needs. For example, a summarization system for product reviews might require polarity classification at the sentence or phrase level; a question answering sys- tem would most likely require the sentiment of para- graphs; 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 example, This is the first Mp3 player that I have used ... I thought it sounded great ... After only a few weeks, it started having trouble with the earphone connec- tion ... I won’t be buying another. Mp3 player review from Amazon.com This excerpt expresses an overall negative opinion of the product being reviewed. However, not all parts of the review are negative. The first sentence merely provides some context on the reviewer’s experience with such devices and the second sentence indicates that, at least in one regard, the product performed well. We call the problem of identifying the senti- ment of the document and of all its subcomponents, whether at the paragraph, sentence, phrase or word level, fine-to-coarse sentiment analysis. The simplest approach to fine-to-coarse sentiment analysis would be to create a separate system for each level of granularity. There are, however, obvi- ous advantages to building a single model that clas- sifies each level in tandem. Consider the sentence, …

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