tailieunhanh - Báo cáo khoa học: "Latent Variable Models for Semantic Orientations of Phrases"

We propose models for semantic orientations 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 component words. Some words can invert the orientation. In order to capture the property of such phrases, we introduce latent variables into the models. Through experiments, we show that the proposed latent variable models work well in the classification of semantic orientations of phrases and achieved nearly 82% classification accuracy. . | Latent Variable Models for Semantic Orientations of Phrases Hiroya Takamura Takashi Inui Precision and Intelligence Laboratory Japan Society of the Promotion of Science Tokyo Institute of Technology tinui@ takamura@ Manabu Okumura Precision and Intelligence Laboratory Tokyo Institute of Technology oku@ Abstract We propose models for semantic orientations 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 component words. Some words can invert the orientation. In order to capture the property of such phrases we introduce latent variables into the models. Through experiments we show that the proposed latent variable models work well in the classification of semantic orientations of phrases and achieved nearly 82 classification accuracy. 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. Automatic sentiment analysis enables a fast and comprehensive investigation. The most fundamental step for sentiment analysis is to acquire the semantic orientations of words desirable or undesirable positive or negative . For example the word beautiful is positive 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 semantic orientations of phrases or multi-term expressions. No computational model for semanti cally oriented phrases has been proposed so far although some researchers have used techniques developed for single words. The purpose of this paper is to propose computational models for