The emotional arcs of stories are dominated by six basic shapes

• Published in: EPJ data science Vol. 5; no. 1; pp. 31 - 12
• Main Authors: Reagan, Andrew J, Mitchell, Lewis, Kiley, Dilan, Danforth, Christopher M, Dodds, Peter Sheridan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 04.11.2016; Springer Nature B.V; SpringerOpen
• Subjects: Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Data-driven Science; Emotions; language; Modeling and Theory Building; narratology; Natural language processing; Regular Article; sentiment mining; society; stories; Supervised learning; Unsupervised learning; society; language; stories; narratology; sentiment mining
• ISSN: 2193-1127; 2193-1127
• DOI: 10.1140/epjds/s13688-016-0093-1

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.