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Modeling Modernist Dialogism: Close Reading with Big Data

  • Adam HammondEmail author
  • Julian Brooke
  • Graeme Hirst
Chapter

Abstract

In Macroanalysis (2013), Matthew Jockers provocatively declares that large digitized collections of literary texts have rendered close reading “totally inappropriate as a method of studying literary history.” Hammond, Brooke and Hirst respond by demonstrating the productive interpretive interplay that results when close reading is placed in a “feedback loop” with the insights available at the scale of big data. Using cutting-edge techniques in computational stylistics, including their own six-dimensional approach to quantifying literary style, Hammond, Brooke and Hirst argue that analytic techniques trained on large datasets can prompt new close readings and, in particular, provide new insight into the dialogism or multi-voicedness of three important modernist texts: T. S. Eliot’s The Waste Land, Virginia Woolf’s To the Lighthouse and James Joyce’s “The Dead.”

Keywords

Latent Semantic Analysis Literary History Character Speech Close Reading Direct Discourse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.San Diego State UniversitySan DiegoUSA
  2. 2.University of MelbourneMelbourneAustralia
  3. 3.University of TorontoTorontoCanada

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