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Scientific Study of Literature
Vol. 7:1 (2017) ► pp.4–51

What’s in the brain that ink may character ….

A quantitative narrative analysis of Shakespeare’s 154 sonnets for use in (Neuro-)cognitive poetics

| Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany | Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.), Berlin, Germany | Center for Cognitive Neuroscience Berlin (CCNB), Berlin, Germany
| Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany | Universität Salzburg, Centre for Cognitive Neuroscience, Salzburg, Austria
| Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany
| Department of Experimental and Neurocognitive Psychology, Freie Universität, Berlin, Germany | Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.), Berlin, Germany

In this theoretical paper, we would like to pave the ground for future empirical studies in Neurocognitive Poetics by describing relevant properties of Shakespeare’s 154 sonnets extracted via Quantitative Narrative Analysis. In the first two parts, we quantify aspects of the sonnets’ cognitive and affective-aesthetic features, as well as indices of their thematic richness, symbolic imagery, and semantic association potential. In the final part, we first demonstrate how the results of these quantitative narrative analyses can be used for generating testable predictions for empirical studies of literature. Second, we feed the quantitative narrative analysis data into a machine learning algorithm which successfully classifies the 154 sonnets into two main categories, i.e. the young man and dark lady poems. This shows how quantitative narrative analysis data can be combined with computational modeling for identifying those of the many quantifiable sonnet features that may play a key role in their reception.

Keywords: neurocognitive poetics, quantitative narrative analysis, Coh-metrix, SEANCE, machine learning, digital humanities, sonnets
Article outline
  • General features of sonnets
  • Cognitive, evolutionary, and quantitative narrative analyses of Shakespeare’s dramas and sonnets
  • The present study
    • Part I.Cognitive quantitative narrative analyses: Readability and easability analyses
      • Surprisal
    • Part II.Affective-aesthetic quantitative narrative analyses
      • Emotion and mood potential
      • Thematic richness
      • Symbolic imagery
      • Semantic association potential
    • Can quantitative narrative analysis capture sonnet dynamics?
    • Part III.Hypotheses for neurocognitive poetics studies
      • What can the present quantitative narrative analyses be used for?
      • Predictions based on present results
      • Poem category
      • Across-poem contrasts
      • Within-poem contrasts (e.g., quatrain 1–3 vs. couplet, octave vs. sestet, or linewise)
    • Part IV.Machine-learning-based computational modeling
      • Classifying the sonnets via machine-learning assisted quantitative narrative analysis
  • Discussion, limitations and outlook
  • Notes
  • References


This article is currently available as a sample article.

Published online: 23 November 2017
DOI logo
https://doi.org/10.1075/ssol.7.1.02jac
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Bruhn, Mark J.
2018. Citation analysis. Scientific Study of Literature 8:1 ► pp. 77 ff. DOI logo
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2024. On the Complexity of Literary and Popular Fiction. Empirical Studies of the Arts 42:1 ► pp. 281 ff. DOI logo
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2019. The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap. Behavior Research Methods 51:1 ► pp. 14 ff. DOI logo
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Jacobs, Arthur M.
2017. Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective. Frontiers in Human Neuroscience 11 DOI logo
Jacobs, Arthur M.
2018. The Gutenberg English Poetry Corpus: Exemplary Quantitative Narrative Analyses. Frontiers in Digital Humanities 5 DOI logo
Jacobs, Arthur M.
2018. (Neuro-)Cognitive poetics and computational stylistics. Scientific Study of Literature 8:1 ► pp. 165 ff. DOI logo
Jacobs, Arthur M.
2019. Sentiment Analysis for Words and Fiction Characters From the Perspective of Computational (Neuro-)Poetics. Frontiers in Robotics and AI 6 DOI logo
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2018. What makes a metaphor literary? Answers from two computational studies. Metaphor and Symbol 33:2 ► pp. 85 ff. DOI logo
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2020. From Abstract Symbols to Emotional (In-)Sights: An Eye Tracking Study on the Effects of Emotional Vignettes and Pictures. Frontiers in Psychology 11 DOI logo
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