Identifying and Analyzing Judgment Opinions

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 200–207, New York, June 2006. c⃝2006 Association for Computational Linguistics Identifying and Analyzing Judgment Opinions Soo-Min Kim and Eduard Hovy USC Information Sciences Institute 4676 Admiralty Way, Marina del Rey, CA 90292 {skim, hovy}@ISI.EDU Abstract In this paper, we introduce a methodology for analyzing judgment opinions. We de- fine a judgment opinion as consisting of a valence, a holder, and a topic. We decom- pose the task of opinion analysis into four parts: 1) recognizing the opinion; 2) iden- tifying the valence; 3) identifying the holder; and 4) identifying the topic. In this paper, we address the first three parts and evaluate our methodology using both in- trinsic and extrinsic measures. 1 Introduction Recently, many researchers and companies have explored the area of opinion detection and analysis. With the increased immersion of Internet users has come a proliferation of opinions available on the web. Not only do we read more opinions from the web, such as in daily news editorials, but also we post more opinions through mechanisms such as governmental web sites, product review sites, news group message boards and personal blogs. This phenomenon has opened the door for massive opinion collection, which has potential impact on various applications such as public opinion moni- toring and product review summary systems. Although in its infancy, many researchers have worked in various facets of opinion analysis. Pang et al. (2002) and Turney (2002) classified senti- ment polarity of reviews at the document level. Wiebe et al. (1999) classified sentence level sub- jectivity using syntactic classes such as adjectives, pronouns and modal verbs as features. Riloff and Wiebe (2003) extracted subjective expressions from sentences using a bootstrapping pattern learn- ing process. Yu and Hatzivassiloglou (2003) iden- tified the polarity of opinion sentences using semantically oriented words. These techniques were applied and examined in different domains, such as customer reviews (Hu and Liu 2004) and news articles1. These researchers use lists of opin- ion-bearing clue words and phrases, and then apply various additional techniques and refinements. Along with many opinion researchers, we par- ticipated in a large pilot study, sponsored by NIST, which concluded that it is very difficult to define what an opinion is in general. Moreover, an ex- pression that is considered as an opinion in one domain might not be an opinion in another. For example, the statement “The screen is very big” might be a positive review for a wide screen desk- top review, but it could be a mere fact in general newspaper text. This implies that it is hard to apply opinion bearing words collected from one domain to an application for another domain. One might therefore need to collect opinion clues within indi- vidual domains. In case we cannot simply find training data from existing sources, such as news article analysis, we need to manually annotate data first. Most opinions are of two kinds: 1) beliefs about the world, with …

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