Article published in:
Empirical Studies of Literariness
Edited by Massimo Salgaro and Paul Sopčák
[Scientific Study of Literature 8:1] 2018
► pp. 165–208

(Neuro-)Cognitive poetics and computational stylistics

| Freie Universität Berlin
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.
Keywords: neurocognitive poetics, quantitative narrative analysis, computational stylistics, digital literary studies, neuroaesthetics, affective-aesthetic processes, literary reading
Article outline
  • Towards representative and reliable training corpora in several languages
  • The training corpus as a statistical reader model
  • QNA and machine learning tools for extracting and determining the importance of text features
  • Well selected, ecologically valid literary test materials and reliable open-access reader-response data banks
  • Illustrative studies merging (neuro-)cognitive poetics and computational stylistics
    • Study 1. Lexical distinctiveness
    • Study 2. Lexical diversity and concreteness
    • Study 3. Sentiment analysis and lexical aesthetic potential
    • Study 4. Affective sublexical indices: Tender vs. aggressive consonants and sonority score
    • Study 5. Interlexical indices: Bigram novelty, tenor vehicle similarity, and arousal span
    • Study 6. Supralexical indices (lines, sentences, stanzas, poem)
      • Lines
      • Sentences
      • Stanza dynamics
      • Topics
  • Discussion and outlook
  • Notes
  • References
Published online: 17 January 2019
https://doi.org/10.1075/ssol.18002.jac
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Cited by

Cited by 7 other publications

Jacobs, Arthur M.
2019. Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics. Frontiers in Robotics and AI 6 Crossref logo
Jacobs, Arthur M., Berenike Herrmann, Gerhard Lauer, Jana Lüdtke & Sascha Schroeder
2020. Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect?. Frontiers in Psychology 11 Crossref logo
Jacobs, Arthur M. & Annette Kinder
2019. Computing the Affective-Aesthetic Potential of Literary Texts. AI 1:1 ► pp. 11 ff. Crossref logo
Jacobs, Arthur M. & Annette Kinder
2022. Computational Models of Readers' Apperceptive Mass. Frontiers in Artificial Intelligence 5 Crossref logo
Papp-Zipernovszky, Orsolya, Anne Mangen, Arthur Jacobs & Jana Lüdtke
2021. Shakespeare sonnet reading: An empirical study of emotional responses. Language and Literature: International Journal of Stylistics ► pp. 096394702110546 ff. Crossref logo
Usée, Franziska, Arthur M. Jacobs & Jana Lüdtke
2020. From Abstract Symbols to Emotional (In-)Sights: An Eye Tracking Study on the Effects of Emotional Vignettes and Pictures. Frontiers in Psychology 11 Crossref logo
Xue, Shuwei, Arthur M. Jacobs & Jana Lüdtke
2020. What Is the Difference? Rereading Shakespeare’s Sonnets —An Eye Tracking Study. Frontiers in Psychology 11 Crossref logo

This list is based on CrossRef data as of 23 april 2022. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.