Yuri Bizzoni ; Telma Peura ; Mads Thomsen ; Kristoffer Nielbo - Fractal Sentiments and Fairy Tales - Fractal scaling of narrative arcs as predictor of the perceived quality of Andersen's fairy tales

jdmdh:9154 - Journal of Data Mining & Digital Humanities, June 1, 2022, NLP4DH - https://doi.org/10.46298/jdmdh.9154
Fractal Sentiments and Fairy Tales - Fractal scaling of narrative arcs as predictor of the perceived quality of Andersen's fairy tales

Authors: Yuri Bizzoni ; Telma Peura ; Mads Thomsen ; Kristoffer Nielbo

This article explores the sentiment dynamics present in narratives and their contribution to literary appreciation. Specifically, we investigate whether a certain type of sentiment development in a literary narrative correlates with its quality as perceived by a large number of readers. While we do not expect a story's sentiment arc to relate directly to readers' appreciation, we focus on its internal coherence as measured by its sentiment arc's level of fractality as a potential predictor of literary quality. To measure the arcs' fractality we use the Hurst exponent, a popular measure of fractal patterns that reflects the predictability or self-similarity of a time series. We apply this measure to the fairy tales of H.C. Andersen, using GoodReads' scores to approximate their level of appreciation. Based on our results we suggest that there might be an optimal balance between predictability and surprise in a sentiment arcs' structure that contributes to the perceived quality of a narrative text.


Volume: NLP4DH
Published on: June 1, 2022
Accepted on: April 12, 2022
Submitted on: March 1, 2022
Keywords: fractal analysis,sentiment analysis,computational narratology,literary quality assessment,stylometry,[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]


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