  Advances in computing power, natural language processing, and digitization of
text now make it possible to study a culture's evolution through its texts
using a "big data" lens. Our ability to communicate relies in part upon a
shared emotional experience, with stories often following distinct emotional
trajectories and forming patterns that are meaningful to us. Here, by
classifying the emotional arcs for a filtered subset of 1,327 stories from
Project Gutenberg's fiction collection, we find a set of six core emotional
arcs which form the essential building blocks of complex emotional
trajectories. We strengthen our findings by separately applying Matrix
decomposition, supervised learning, and unsupervised learning. For each of
these six core emotional arcs, we examine the closest characteristic stories in
publication today and find that particular emotional arcs enjoy greater
success, as measured by downloads.
