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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. . | Mood Patterns and Affective Lexicon Access in Weblogs Thin Nguyen Curtin University of Technology Bentley WA 6102 Australia thin.nguyen@postgrad.curtin.edu.au Abstract 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. 1 Introduction Social media provides communication and interaction channels where users can freely participate in express their opinions make their own content and interact with other users. Users in this new media are more comfortable in expressing their feelings opinions and ideas. Thus the resulting user-generated content tends to be more subjective than other written genres and thus is more appealing to be investigated in terms of subjectivity and sentiment analysis. Research in sentiment analysis has recently attracted much attention Pang and Lee 2008 but modeling emotion patterns and studying the affective lexicon used in social media have received little attention. Work in sentiment analysis in social media is often limited to finding the sentiment sign in the dipole pattern negative positive for given text. Extensions to this task include the three-class classification adding neutral to the polarity and .
