Smart Software Figures Out What Makes Stories Tick
November 28, 2016
I recall sitting in high school when I was 14 years old and listening to our English teacher explain the basic plots used by fiction writers. The teacher was Miss Dalton and he seemed quite happy to point out that fiction depended upon: Man versus man, man versus the environment, man versus himself, man versus belief, and maybe one or two others. I don’t recall the details of a chalkboard session in 1959.
Not to fear.
I read “Fiction Books Narratives Down to Six Emotional Story Lines.” Smart software and some PhDs have cracked the code. Ivory Tower types processed digital versions of 1,327 books of fiction. I learned:
They [the Ivory Tower types] then applied three different natural language processing filters used for sentiment analysis to extract the emotional content of 10,000-word stories. The first filter—dubbed singular value decomposition—reveals the underlying basis of the emotional storyline, the second—referred to as hierarchical clustering—helps differentiate between different groups of emotional storylines, and the third—which is a type of neural network—uses a self-learning approach to sort the actual storylines from the background noise. Used together, these three approaches provide robust findings, as documented on the hedonometer.org website.
Okay, and what’s the smart software say today that Miss Dalton did not tell me more than 50 years ago?
[The Ivory Tower types] determined that there were six main emotional storylines. These include ‘rags to riches’ (sentiment rises), ‘riches to rags’ (fall), ‘man in a hole’ (fall-rise), ‘Icarus’ (rise-fall), ‘Cinderella’ (rise-fall-rise), ‘Oedipus’ (fall-rise-fall). This approach could, in turn, be used to create compelling stories by gaining a better understanding of what has previously made for great storylines. It could also teach common sense to artificial intelligence systems.
Ah, progress.
Stephen E Arnold, November 28, 2016
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