Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

• Main Author: Arthur M. Jacobs
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2017-12-01
• Series: Frontiers in Human Neuroscience
• Subjects: neurocognitive poetics; quantitative narrative analysis; machine learning; digital humanities; neuroaesthetics; computational stylistics
• Online Access: http://journal.frontiersin.org/article/10.3389/fnhum.2017.00622/full

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.