What’s in the brain that ink may character ….
A quantitative narrative analysis of Shakespeare’s 154 sonnets for use in (Neuro-)cognitive poetics
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.
- 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
- 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
- Part I.Cognitive quantitative narrative analyses: Readability and easability analyses
- Discussion, limitations and outlook
This article is currently available as a sample article.
Cited by 14 other publications
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