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Emotion analysis using latent affective folding and embedding
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Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon
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Emotional sequencing and development in fairy tales
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Tracking sentiment in mail: how genders differ on emotional axes
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Tracking sentiment in mail: how genders differ on emotional axes
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
From once upon a time to happily ever after: Tracking emotions in mail and books
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Portable features for classifying emotional text
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Distant supervision for emotion classification with discrete binary values
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Using google n-grams to expand word-emotion association lexicon
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SentiML: functional annotation for multilingual sentiment analysis
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Today we have access to unprecedented amounts of literary texts. However, search still relies heavily on key words. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in both individual books and across very large collections. We introduce the concept of emotion word density, and using the Brothers Grimm fairy tales as example, we show how collections of text can be organized for better search. Using the Google Books Corpus we show how to determine an entity's emotion associations from co-occurring words. Finally, we compare emotion words in fairy tales and novels, to show that fairy tales have a much wider range of emotion word densities than novels.