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
