Fractal scaling laws for the dynamic evolution of sentiments in Never Let Me Go and their implications for writing, adaptation and reading of novels

Abstract

A good novel can often elicit from a reader strong sentiments similar to the moods, feelings, and attitudes depicted in the novel. With the rapid progress in AI, sentiment-based arcs in novels can now be reliably extracted and used to summarize the novel’s plot in the story arc. Are there salient mathematical properties that underlie such story arcs and have far-reaching implications in the writing, adaptation, and reading of the novel? To gain insights into this question, we employ multifractal theory to characterize the narrative coherence and dynamic evolution of sentiments of the novel, Never Let Me Go, by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an example. Three methods are compared for fractal scaling analysis, the classic variance-time method, an improvement of the variance-time relation based on adaptive filtering, and adaptive fractal analysis. We find that while variance-time relation fails to accurately extract the fractal scaling exponent, adaptive fractal analysis succeeds in fully characterizing the fractal variations in the sentiment dynamics. The finding may be indicative of the potential that multifractal theory has for computational narratology and large-scale literary analysis, especially for inferring the degree of narrative coherence and variation of the plot of a novel.