Fractal sentiments and fairy tales-fractal scaling of narrative arcs as predictor of the perceived quality of Andersen’s fairy tales

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...

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Published in:Journal of data mining and digital humanities Vol. NLP4DH
Main Authors: Bizzoni, Yuri, Peura, Telma, Thomsen, Mads, Nielbo, Kristoffer
Format: Journal Article
Language:English
Published: INRIA 01.06.2022
Nicolas Turenne
Subjects:
[info.info-cl]computer science [cs]/computation and language [cs.cl]
Computation and Language
computational narratology
Computer Science
fractal analysis
literary quality assessment
sentiment analysis
stylometry
stylometry
fractal analysis
literary quality assessment
sentiment analysis
computational narratology
ISSN:2416-5999, 2416-5999
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Nielbo, Kristoffer
Peura, Telma
Bizzoni, Yuri
Thomsen, Mads
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Keywords stylometry
fractal analysis
literary quality assessment
sentiment analysis
computational narratology
Language English
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Nicolas Turenne
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SubjectTerms [info.info-cl]computer science [cs]/computation and language [cs.cl]
Computation and Language
computational narratology
Computer Science
fractal analysis
literary quality assessment
sentiment analysis
stylometry
Title Fractal sentiments and fairy tales-fractal scaling of narrative arcs as predictor of the perceived quality of Andersen’s fairy tales
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