Latent Variable Models for Semantic Orientations of Phrases

Latent Variable Models for Semantic Orientations of Phrases Hiroya Takamura Precision and Intelligence Laboratory Tokyo Institute of Technology takamura@pi.titech.ac.jp Takashi Inui Japan Society of the Promotion of Science tinui@lr.pi.titech.ac.jp Manabu Okumura Precision and Intelligence Laboratory Tokyo Institute of Technology oku@pi.titech.ac.jp Abstract We propose models for semantic orienta- tions of phrases as well as classification methods based on the models. Although each phrase consists of multiple words, the semantic orientation of the phrase is not a mere sum of the orientations of the com- ponent words. Some words can invert the orientation. In order to capture the prop- erty of such phrases, we introduce latent variables into the models. Through exper- iments, we show that the proposed latent variable models work well in the classifi- cation of semantic orientations of phrases and achieved nearly 82% classification ac- curacy. 1 Introduction Technology for affect analysis of texts has recently gained attention in both academic and industrial areas. It can be applied to, for example, a survey of new products or a questionnaire analysis. Au- tomatic sentiment analysis enables a fast and com- prehensive investigation. The most fundamental step for sentiment anal- ysis is to acquire the semantic orientations of words: desirable or undesirable (positive or neg- ative). For example, the word “beautiful” is pos- itive, while the word “dirty” is negative. Many researchers have developed several methods for this purpose and obtained good results (Hatzi- vassiloglou and McKeown, 1997; Turney and Littman, 2003; Kamps et al., 2004; Takamura et al., 2005; Kobayashi et al., 2001). One of the next problems to be solved is to acquire se- mantic orientations of phrases, or multi-term ex- pressions. No computational model for semanti- cally oriented phrases has been proposed so far al- though some researchers have used techniques de- veloped for single words. The purpose of this pa- per is to propose computational models for phrases with semantic orientations as well as classification methods based on the models. Indeed the seman- tic orientations of phrases depend on context just as the semantic orientations of words do, but we would like to obtain the most basic orientations of phrases. We believe that we can use the obtained basic orientations of phrases for affect analysis of higher linguistic units such as sentences and doc- uments. The semantic orientation of a phrase is not a mere sum of its component words. Semantic orientations can emerge out of combinations of non-oriented words. For example, “light laptop- computer” is positively oriented although neither “light” nor “laptop-computer” has a positive ori- entation. Besides, some words can invert the ori- entation of a neighboring word, such as “low” in “low risk”, where the negative orientation of “risk” is inverted to a “positive” by the adjective “low”. This kind of non-compositional operation has to be incorporated into the model. We focus on “noun+adjective” in this paper, since this type of phrase contains most of interesting properties of phrases, such as emergence or inversion of se- mantic orientations. In order to capture the properties …

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