Journal
POETICS
Volume 41, Issue 6, Pages 750-769Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.poetic.2013.08.005
Keywords
19th-Century literature; Topic modeling; Machine learning
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External factors such as author gender, author nationality, and date of publication can affect both the choice of literary themes in novels and the expression of those themes, but the extent of this association is difficult to quantify. In this work, we apply statistical methods to identify and extract hundreds of topics (themes) from a corpus of 19th-century British, Irish, and American fiction. We use these topics as a measurable, data-driven proxy for literary themes and assess how external factors may predict fluctuations in the use of themes and the individual word choices within themes. We use topics not only to measure these associations but also to evaluate whether this evidence is statistically significant. (C) 2013 Elsevier B.V. All rights reserved.
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