Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

In this perspective paper I would like to pave the ground for future empirical studies in (Neuro-)Cognitive Poetics by describing procedures for predicting the subjective beauty of words. A set of eight relevant features of words is computed via Quantitative Narrative Analysis (QNA) and a novel metric for quantifying word beauty, the affective-aesthetic value 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 relevant for affective-aesthetic processes in literary reading and generate quantitative predictions for neuroaesthetic studies of verbal materials.