Back in 1995, Kurt Vonnegut gave a lecture in which he described his theory about the shapes of stories. In the process, he plotted several examples on a blackboard. “There is no reason why the simple shapes of stories can’t be fed into computers,” he said. “They are beautiful shapes.”
Vonnegut was representing in graphical form an idea that writers have explored for centuries that stories follow emotional arcs, that these arcs can have different shapes, and that some shapes are better suited to storytelling than others. Vonnegut mapped out several arcs in his lecture.
Vonnegut is not alone in attempting to categorize stories into types, although he was probably the first to do it in graphical form. Aristotle was at it over 2,000 years before him, and many others have followed in his footsteps.
However, there is little agreement on the number of different emotional arcs that arise in stories or their shape. Estimates vary from three basic patterns to more than 30. But there is little in the way of scientific evidence to favor one number over another.
Today, that changes thanks to the work of Andrew Reagan at the Computational Story Lab at the University of Vermont in Burlington and a few pals. These guys have used sentiment analysis to map the emotional arcs of over 1,700 stories and then used data-mining techniques to reveal the most common arcs. “We find a set of six core trajectories which form the building blocks of complex narratives,” they say.
Their method is straightforward. The idea behind sentiment analysis is that words have a positive or negative emotional impact. So words can be a measure of the emotional valence of the text and how it changes from moment to moment. So measuring the shape of the story arc is simply a question of assessing the emotional polarity of a story at each instant and how it changes.
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