Extracting Semantic Orientations of Words using Spin Model

Proceedings of the 43rd Annual Meeting of the ACL, pages 133–140, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Extracting Semantic Orientations of Words using Spin Model Hiroya Takamura Takashi Inui Manabu Okumura Precision and Intelligence Laboratory Tokyo Institute of Technology 4259 Nagatsuta Midori-ku Yokohama, 226-8503 Japan {takamura,oku}@pi.titech.ac.jp, tinui@lr.pi.titech.ac.jp Abstract We propose a method for extracting se- mantic orientations of words: desirable or undesirable. Regarding semantic ori- entations as spins of electrons, we use the mean field approximation to compute the approximate probability function of the system instead of the intractable ac- tual probability function. We also pro- pose a criterion for parameter selection on the basis of magnetization. Given only a small number of seed words, the pro- posed method extracts semantic orienta- tions with high accuracy in the exper- iments on English lexicon. The result is comparable to the best value ever re- ported. 1 Introduction Identification of emotions (including opinions and attitudes) in text is an important task which has a va- riety of possible applications. For example, we can efficiently collect opinions on a new product from the internet, if opinions in bulletin boards are auto- matically identified. We will also be able to grasp people’s attitudes in questionnaire, without actually reading all the responds. An important resource in realizing such identifi- cation tasks is a list of words with semantic orienta- tion: positive or negative (desirable or undesirable). Frequent appearance of positive words in a docu- ment implies that the writer of the document would have a positive attitude on the topic. The goal of this paper is to propose a method for automatically cre- ating such a word list from glosses (i.e., definition or explanation sentences ) in a dictionary, as well as from a thesaurus and a corpus. For this purpose, we use spin model, which is a model for a set of elec- trons with spins. Just as each electron has a direc- tion of spin (up or down), each word has a semantic orientation (positive or negative). We therefore re- gard words as a set of electrons and apply the mean field approximation to compute the average orienta- tion of each word. We also propose a criterion for parameter selection on the basis of magnetization, a notion in statistical physics. Magnetization indicates the global tendency of polarization. We empirically show that the proposed method works well even with a small number of seed words. 2 Related Work Turney and Littman (2003) proposed two algorithms for extraction of semantic orientations of words. To calculate the association strength of a word with pos- itive (negative) seed words, they used the number of hits returned by a search engine, with a query consisting of the word and one of seed words (e.g., “word NEAR good”, “word NEAR bad”). They re- garded the difference of two association strengths as a measure of semantic orientation. They also pro- posed to use Latent Semantic Analysis to compute the association strength with seed words. An em- pirical …

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