From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales
This work addresses the need for better search and organization in large text collections, such as literary databases, but is incremental as it applies existing sentiment analysis techniques to new domains.
The paper tackles the problem of limited search capabilities in literary texts by using sentiment analysis and visualizations to quantify and track emotions, showing that fairy tales have a wider range of emotion word densities than novels.
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.