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Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective.

ABSTRACT: In this paper I would like to pave the ground for future studies in Computational Stylistics and (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight tentative word features is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the aesthetic potential is proposed. Application of machine learning algorithms fed with this QNA data shows that a classifier of the decision tree family excellently learns to split words into beautiful vs. ugly ones. The results shed light on surface and semantic features theoretically relevant for affective-aesthetic processes in literary reading and generate quantitative predictions for neuroaesthetic studies of verbal materials.

SUBMITTER: Jacobs AM

PROVIDER: S-EPMC5742167 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective.

Jacobs Arthur M AM

Frontiers in human neuroscience 20171219

In this paper I would like to pave the ground for future studies in Computational Stylistics and (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight tentative word features is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the <i>aesthetic potential</i> is proposed. Application of machine learning algorithms fed with this QNA data shows that a classifier of the decision tree family ex ...[more]

PMID: 29311877

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