The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series. Issue 2 (27th December 2015)

• Record Type: Journal Article
• Authors: Buurma, Rachel Sagner
• Is Part Of: Big data & society. Volume 2:Issue 2(2015)
• Publication Date: 2015-12-27
• Subjects: Topic modeling -- literature -- distant reading -- literary criticism -- novels -- machine learning; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/2053951715610591
• Languages: English
• ISSNs: 2053-9517

Abstract

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.