Self-Organizing Maps
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
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Inferring mood in ubiquitous conversational video
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
Social reader: towards browsing the social web
Multimedia Tools and Applications
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Automatic data-driven analysis of mood from text is an emerging problem with many potential applications Unlike generic text categorization, mood classification based on textual features is complicated by various factors, including its context- and user-sensitive nature We present a comprehensive study of different feature selection schemes in machine learning for the problem of mood classification in weblogs Notably, we introduce the novel use of a feature set based on the affective norms for English words (ANEW) lexicon studied in psychology This feature set has the advantage of being computationally efficient while maintaining accuracy comparable to other state-of-the-art feature sets experimented with In addition, we present results of data-driven clustering on a dataset of over 17 million blog posts with mood groundtruth Our analysis reveals an interesting, and readily interpreted, structure to the linguistic expression of emotion, one that comprises valuable empirical evidence in support of existing psychological models of emotion, and in particular the dipoles pleasure–displeasure and activation–deactivation.