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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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Cited by (14)

Cited by 14 other publications

Castano, Emanuele, Jessica Zanella, Fatemeh Saedi, Lisa Zunshine & Luca Ducceschi
2024. On the Complexity of Literary and Popular Fiction. Empirical Studies of the Arts 42:1 ► pp. 281 ff. DOI logo
Al Mamun, Md Habib, Pantea Keikhosrokiani, Moussa Pourya Asl, Nur Ain Nasuha Anuar, Nurfarah Hadira Abdul Hadi & Thasnim Humida
2022. Sentiment Analysis of the Harry Potter Series Using a Lexicon-Based Approach. In Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media [Advances in Web Technologies and Engineering, ], ► pp. 263 ff. DOI logo
Barbado, Alberto, Víctor Fresno, Ángeles Manjarrés Riesco & Salvador Ros
2022. DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels. Language Resources and Evaluation 56:2 ► pp. 501 ff. DOI logo
Papp-Zipernovszky, Orsolya, Anne Mangen, Arthur Jacobs & Jana Lüdtke
2022. Shakespeare sonnet reading: An empirical study of emotional responses. Language and Literature: International Journal of Stylistics 31:3 ► pp. 296 ff. DOI 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 DOI 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 DOI logo
Crossley, Scott A., Kristopher Kyle & Mihai Dascalu
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
Bruhn, Mark J.
2018. Citation analysis. Scientific Study of Literature 8:1 ► pp. 77 ff. DOI logo
Jacobs, Arthur M. & Annette Kinder
2018. What makes a metaphor literary? Answers from two computational studies. Metaphor and Symbol 33:2 ► pp. 85 ff. DOI logo
Hanauer, David Ian
2017. Becoming an undergraduate scientific researcher of literature. Scientific Study of Literature 7:2 ► pp. 262 ff. DOI logo
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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