The emotional arcs of stories are dominated by six basic shapes
This provides insights into storytelling patterns for cultural analysis and literary studies, though it is incremental in applying data science to existing texts.
The study analyzed emotional arcs in 1,327 fiction stories from Project Gutenberg and identified six core patterns that serve as building blocks for complex emotional trajectories, with some arcs correlating with higher download success.
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.