The fictionality of topic modeling

Machine reading Anthony Trollope's Barsetshire series

Bibliographic Data

ID5260977
AuthorsRachel Sagner Buurma (0000-0003-1517-0195, Swarthmore College, corresponding author)
Year2015
Volume2
Issue2
Publication date2015-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/2053951715610591
OpenAlexW2203480748
LanguageEN
Citations received8
References cited9

This essay describes how using unsupervised topic modeling (specifically the latent Dirichlet allocation topic modeling algorithm in MALLET) on relatively small corpuses can help scholars of literature circumvent the limitations of some existing theories of the novel. Using an example drawn from work on Victorian novelist Anthony Trollope's Barsetshire series, it argues that unsupervised topic modeling's counter-factual and retrospective reconstruction of the topics out of which a given set of novels have been created allows for a denaturalizing and unfamiliar (though crucially not "objective" or "unbiased") view. In other words, topic models are fictions, and scholars of literature should consider reading them as such. Drawing on one aspect of Stephen Ramsay's idea of algorithmic criticism, the essay emphasizes the continuities between "big data" methods and techniques and longer-standing methods of literary study

Art · Criticism · Data science · Latent Dirichlet allocation · Linguistics · Literature · Topic model · Authorship Attribution and Profiling · Computational and Text Analysis Methods · Computer Science · Digital Humanities and Scholarship · Philosophy · Artificial Intelligence

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