Dynamic evolution of sentiments in Never Let Me Go: Insights from quantitative analysis and implications

Abstract

The moods, feelings and attitudes represented in a novel will resonate in the reader by activating similar sentiments. It is generally accepted that sentiment analysis can capture aspects of such moods, feelings and attitudes and can be used to summarize a novel's plot in a story arc. With the availability of a number of algorithms to automatically extract sentiment-based arcs, new approaches for their utilization becomes pertinent. We propose to use nonlinear adaptive filtering and fractal analysis in order to analyze the narrative coherence and dynamic evolution of a novel. We propose to use Never Let Me Go by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an example, we show that: 1) nonlinear adaptive filtering extracts a story arc that reflects the tragic trend of the novel; 2) The story arc displays persistent dynamics as measured by the Hurst exponent at short and medium time scales; 3) the plots' dynamic evolution is reflected in the time-varying Hurst exponent. We argue that these findings are indicative of the potential multifractal theory has for computational narratology and large-scale literary analysis. Specifically, that the global Hurst exponent of a story arc is an index of narrative coherence that can identify bland, incoherent and coherent narratives on a continuous scale. And, further, that the local time-varying Hurst exponent captures variation of a novel's plot such that the extreme have specific narratological interpretations.