Extracting Product Features and Opinions from Reviews

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 339–346, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Extracting Product Features and Opinions from Reviews Ana-Maria Popescu and Oren Etzioni Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350 {amp, etzioni}@cs.washington.edu Abstract Consumers are often forced to wade through many on-line reviews in order to make an informed prod- uct choice. This paper introduces OPINE, an unsupervised information- extraction system which mines re- views in order to build a model of im- portant product features, their evalu- ation by reviewers, and their relative quality across products. Compared to previous work, OPINE achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. OPINE’s novel use of relaxation labeling for finding the se- mantic orientation of words in con- text leads to strong performance on the tasks of finding opinion phrases and their polarity. 1 Introduction The Web contains a wealth of opinions about products, politicians, and more, which are expressed in newsgroup posts, review sites, and elsewhere. As a result, the prob- lem of “opinion mining” has seen increasing attention over the last three years from (Turney, 2002; Hu and Liu, 2004) and many others. This paper focuses on product reviews, though our methods apply to a broader range of opinions. Product reviews on Web sites such as amazon.com and elsewhere often associate meta-data with each review indicating how positive (or negative) it is using a 5-star scale, and also rank products by how they fare in the re- views at the site. However, the reader’s taste may differ from the reviewers’. For example, the reader may feel strongly about the quality of the gym in a hotel, whereas many reviewers may focus on other aspects of the ho- tel, such as the decor or the location. Thus, the reader is forced to wade through a large number of reviews looking for information about particular features of interest. We decompose the problem of review mining into the following main subtasks: I. Identify product features. II. Identify opinions regarding product features. III. Determine the polarity of opinions. IV. Rank opinions based on their strength. This paper introduces OPINE, an unsupervised infor- mation extraction system that embodies a solution to each of the above subtasks. OPINE is built on top of the Know- ItAll Web information-extraction system (Etzioni et al., 2005) as detailed in Section 3. Given a particular product and a corresponding set of reviews, OPINE solves the opinion mining tasks outlined above and outputs a set of product features, each accom- panied by a list of associated opinions which are ranked based on strength (e.g., “abominable” is stronger than “bad). This output information can then be used to gen- erate various types of opinion summaries. This paper focuses on the first 3 review mining sub- tasks and our contributions are as follows: 1. We introduce OPINE, a review-mining system whose novel components include the use of …

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