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  • Computational Poetics is the scientific perspective that analyses literary texts quantitatively by help of state-of-the-art NLP and machine learning methods.
  • Its goal is to understand which text features drive the fluent, immersive or dysfluent, reflective reading of narratives and poetry (cf. Jacobs, 2015, 2018).
  • It focuses on affective-aesthetic features (e.g., Emotion, Immersion and Aesthetic Potential) in their dynamic interaction with cognitive aspects (e.g., processing fluency, syntactic complexity, readability; cf. Jacobs et al., 2017).
  • Key features are analysed at different text levels (e.g., phonological, semantic) and for different types (e.g., sublexical, interlexical features) based on the 4×4 matrix especially designed for ACTA shown in the left-hand figures (cf. Jacobs, 2015, 2018a).
  • The following pages present example applications of computational poetics studies from Emotion Potential to Sound Beauty.