Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Understanding how bloggers feel: recognizing affect in blog posts
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
An affect-enriched dialogue act classification model for task-oriented dialogue
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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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. 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 core-affect 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.