Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective - PubMed (original) (raw)

Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

Arthur M Jacobs. Front Hum Neurosci. 2017.

Erratum in

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.

Keywords: computational stylistics; decision trees; digital humanities; literary reading; machine learning; neuroaesthetics; neurocognitive poetics; quantitative narrative analysis.

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Figures

Figure 1

Figure 1

Confusion matrix (A) and Receiver Operating Characteristic (ROC, B–D) for the ERT classifier (CLF) with eight input variables; (B) original data set with parameters set 1; (C) original data set with parameter set 2 (see Appendix C in Supplementary Material for details); (D) permuted data set. (D) Shows the ROCs for five consecutive runs of the k-fold cross-validation for the randomized data set which were all at chance level.

References

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