Mood Patterns and Affective Lexicon Access in Weblogs

The emergence of social media brings chances, but also challenges, to linguistic analysis. In this paper we investigate a novel problem of discovering patterns based on emotion and the association of moods and affective lexicon usage in blogosphere, a representative for social media. We propose the use of normative emotional scores for English words in combination with a psychological model of emotion measurement and a nonparametric clustering process for inferring meaningful emotion patterns automatically from data. Our results on a dataset consisting of more than 17 million mood-groundtruthed blogposts have shown interesting evidence of the emotion patterns automatically discovered that match well with the coreaffect emotion model theorized by psychologists. We then present a method based on information theory to discover the association of moods and affective lexicon usage in the new media.