Abstract
Advances in computing power, natural language processing, and digitization of
text now make it possible to study our 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, forming patterns that are meaningful to us. Here, by classifying
the emotional arcs for a filtered subset of 1,737 stories from Project
Gutenberg's fiction collection, we find a set of six core trajectories which
form the building blocks of complex narratives. We strengthen our findings by
separately applying optimization, linear 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.
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