Proposal of Impression Mining from News Articles

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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.