Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-12-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00622/full |
| Summary: | 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. |
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| ISSN: | 1662-5161 |