(Neuro-)Cognitive poetics and computational stylistics

This perspective paper discusses four general desiderata of current computational stylistics and (neuro-)cognitive poetics concerning the development of (a) appropriate databases/training corpora, (b) advanced qualitative-quantitative narrative analysis (Q2NA) and machine learning tools for feature extraction, (c) ecologically valid literary test materials, and (d) open-access reader-response data banks. In six explorative computational stylistics studies, it introduces a number of tools that provide QNA indices of the foregrounding potential at the sublexical, lexical, inter- and supralexical levels for poems by Shakespeare, Blake, or Dickens. These concern lexical diversity and aesthetic potential, sentiment analysis, sublexical sonority scores or phrase structure, and topics analysis. The results illustrate the complex interplay of stylistic features and the necessity for theoretical guidance and interdisciplinary cooperation in selecting adequate training corpora, QNA tools, test texts, and response measures.