The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series
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 wor...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2015-12-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/2053951715610591 |
| _version_ | 1818301151007735808 |
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| author | Rachel Sagner Buurma |
| author_facet | Rachel Sagner Buurma |
| author_sort | Rachel Sagner Buurma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T05:18:27Z |
| format | Article |
| id | doaj.art-92e4228b75164ef5aa4ea2a7f6c69091 |
| institution | Directory Open Access Journal |
| issn | 2053-9517 |
| language | English |
| last_indexed | 2024-12-13T05:18:27Z |
| publishDate | 2015-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Big Data & Society |
| spelling | doaj.art-92e4228b75164ef5aa4ea2a7f6c690912022-12-21T23:58:22ZengSAGE PublishingBig Data & Society2053-95172015-12-01210.1177/205395171561059110.1177_2053951715610591The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire seriesRachel Sagner BuurmaThis 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.https://doi.org/10.1177/2053951715610591 |
| spellingShingle | Rachel Sagner Buurma The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series Big Data & Society |
| title | The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series |
| title_full | The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series |
| title_fullStr | The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series |
| title_full_unstemmed | The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series |
| title_short | The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series |
| title_sort | fictionality of topic modeling machine reading anthony trollope s barsetshire series |
| url | https://doi.org/10.1177/2053951715610591 |
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