From once upon a time to happily ever after: Tracking emotions in mail and books

In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in mail and books. We study a number of specific datasets and show, among other things, how collections of texts can be organized for affect-based search and how books portray different entities through co-occurring emotion words. Analysis of the Enron Email Corpus reveals that there are marked differences across genders in how they use emotion words in work-place email. Finally, we show that fairy tales have more extreme emotion densities than novels.