  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.
