Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 355–363, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis Hiroshi Kanayama Tetsuya Nasukawa Tokyo Research Laboratory, IBM Japan, Ltd. 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken, 242-8502 Japan {hkana,nasukawa}@jp.ibm.com Abstract This paper proposes an unsupervised lexicon building method for the detec- tion of polar clauses, which convey pos- itive or negative aspects in a specific domain. The lexical entries to be ac- quired are called polar atoms, the min- imum human-understandable syntactic structures that specify the polarity of clauses. As a clue to obtain candidate polar atoms, we use context coherency, the tendency for same polarities to ap- pear successively in contexts. Using the overall density and precision of co- herency in the corpus, the statistical estimation picks up appropriate polar atoms among candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 94% on average, and our method is ro- bust for corpora in diverse domains and for the size of the initial lexicon. 1 Introduction Sentiment Analysis (SA) (Nasukawa and Yi, 2003; Yi et al., 2003) is a task to recognize writers’ feelings as expressed in positive or negative comments, by analyzing unreadably large numbers of documents. Extensive syn- tactic patterns enable us to detect sentiment expressions and to convert them into seman- tic structures with high precision, as reported by Kanayama et al. (2004). From the exam- ple Japanese sentence (1) in the digital cam- era domain, the SA system extracts a senti- ment representation as (2), which consists of a predicate and an argument with positive (+) polarity. (1) Kono kamera-ha subarashii-to omou. ‘I think this camera is splendid.’ (2) [+] splendid(camera) SA in general tends to focus on subjec- tive sentiment expressions, which explicitly de- scribe an author’s preference as in the above example (1). Objective (or factual) expres- sions such as in the following examples (3) and (4) may be out of scope even though they de- scribe desirable aspects in a specific domain. However, when customers or corporate users use SA system for their commercial activities, such domain-specific expressions have a more important role, since they convey strong or weak points of the product more directly, and may influence their choice to purchase a spe- cific product, as an example. (3) Kontorasuto-ga kukkiri-suru. ‘The contrast is sharp.’ (4) Atarashii kishu-ha zuumu-mo tsuite-iru. ‘The new model has a zoom lens, too.’ This paper addresses the Japanese ver- sion of Domain-oriented Sentiment Analysis, which identifies polar clauses conveying good- ness and badness in a specific domain, in- cluding rather objective expressions. Building domain-dependent lexicons for many domains is much harder work than preparing domain- independent lexicons and syntactic patterns, because the possible lexical entries are too numerous, and they may differ in each do- main. To solve this problem, we have devised an unsupervised method to acquire domain- dependent lexical …

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