Jacobs Arthur M
Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Germany.
Dahlem Institute for Neuroimaging of Emotion, Berlin, Germany.
Front Hum Neurosci. 2017 Dec 19;11:622. doi: 10.3389/fnhum.2017.00622. eCollection 2017.
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 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.
在本文中，我希望通过描述预测词语主观美感的程序，为计算文体学和（神经）认知诗学的未来研究奠定基础。通过定量叙事分析（QNA）计算出一组八个暂定的词语特征，并提出了一种用于量化词语美感的新指标。将机器学习算法应用于这些QNA数据表明，决策树家族的分类器能够出色地学会将词语分为优美与丑陋两类。这些结果揭示了与文学阅读中的情感审美过程理论相关的表面和语义特征，并为言语材料的神经美学研究生成了定量预测。
