Proposal of Impression Mining from News Articles
Each word in a language not only has its own explicit meaning, but also can convey various impressions. In this paper, we focus on the impressions people get from news articles, and propose a method for determining impressions of these news articles. Our proposed method consists of two main parts. One part involves building an “impression dictionary” that describes the relationships among words and impressions. The other part of the method involves determining impressions of input news articles using the impression dictionary. The impressions of a news article are represented as scale values in user-specified impression scales, like “sad – glad” and “angry – pleased”. Each scale value is a real number between 0 and 1, and is calculated from the words (common nouns, action nouns, verbs, adjectives, and katakana characters) extracted from an input news article using the impression dictionary.
Unable to display preview. Download preview PDF.
- 1.Fukui, M., Shibazaki, Y., Sasaki, K., Takebayashi, Y.: Multimodal personal information provider using natural language and emotion understanding from speech and keyboard input. Information Processing Society of Japan SIG Notes, HI64-8, 43–48 (1996)Google Scholar
- 2.Kuraishi, H., Shibata, Y.: Feeling communication system by facial expression analysis/ synthesis using individual models. IPSJ SIG Notes DPS74-14, 79–84 (1996)Google Scholar
- 3.Kurohashi, S., Nagao, M.: Manual of Japanese morphological analysis system Juman version 3.61 (1999), http://pine.kuee.kyoto-u.ac.jp/nl-resource/juman.html
- 5.Nagata, M., Taira, H.: Text classification — Trade fair of learning theories. IPSJ Magazine 42(1), 32–37 (2991)Google Scholar
- 6.Nikkei Newspaper Full Text Database DVD-ROM, 1990 to 1995 editions, 1996 to 2000 editions, 2001 edition, Nihon Keizai Shimbun, Inc. Google Scholar
- 7.Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.In: Proc. Conference on Association for Computational Linguistics (2002)Google Scholar
- 8.Ren, F., Matsumoto, K., Mitsuyoshi, S., Kuroiwa, S., Lin, Y.: Researches on the emotion measurement system. Proc. IEEE International Conference on System, Man and Cybernetics, 1666–1672 (2003)Google Scholar
- 9.SGI Japan Ltd., http://www.sgi.co.jp/newsroom/pressreleases/2004/sep/st.html
- 10.Tsukamoto, K., Sassano, M.: Text categorization using active learning with AdaBoost. IPSJ SIG Notes NL146-13, 81–89 (2001)Google Scholar