The emotional arcs of stories are dominated by six basic shapes

TL;DR

This study identifies six fundamental emotional story arcs from a large dataset of classic fiction, demonstrating their significance and popularity through multiple analytical methods and contemporary examples.

Contribution

The paper introduces a robust classification of six core emotional arcs in stories using diverse machine learning techniques, revealing their foundational role in narrative structure.

Findings

Six core emotional arcs identified across 1,327 stories

Different arcs correlate with higher story popularity

Multiple analytical methods confirm the robustness of the arcs

Abstract

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.