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Analyzing the Positive Sentiment Towards the Term “Queer’’ in Virginia Woolf through a Computational Approach and Close Reading

Shin, Heejoung (2023)
Analyzing the Positive Sentiment Towards the Term “Queer’’ in Virginia Woolf through a Computational Approach and Close Reading.
In: Journal of Computational Literary Studies, 2022, 1 (1)
doi: 10.26083/tuprints-00023262
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Analyzing the Positive Sentiment Towards the Term “Queer’’ in Virginia Woolf through a Computational Approach and Close Reading
Language: English
Date: 2023
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: Universitäts- und Landesbibliothek Darmstadt
Journal or Publication Title: Journal of Computational Literary Studies
Volume of the journal: 1
Issue Number: 1
Collation: 26 Seiten
DOI: 10.26083/tuprints-00023262
Corresponding Links:
Origin: Secondary publication from TUjournals
Abstract:

This article validates the thesis that Virginia Woolf’s usage of the term “queer’’ is positive, and that the author is more progressive with her idea of things conceived as “queer’’ in the era characterized as literary Modernism and in English fiction as a whole from 1850s to 1990s. Using Word2Vec, a word embedding model, I locate the top 100 words semantically closest to “queer’’ in Woolf’s works and in the works of other modernist authors, James Joyce, F. Scott Fitzgerald, D. H. Lawrence, Gertrude Stein, and Katherine Mansfield. I then measure the net positivity of each author’s list and compare Woolf’s with the individual authors’, and then with words closest to “queer’’ in English fiction from 1850 to 2000. In demonstrating the usefulness of applying word embedding models in literary criticism, a field that has traditionally primarily relied on interpretation, this article aims to serve as a case study of how a computational approach can benefit close reading.

Uncontrolled Keywords: Virginia Woolf, queer, modernism, sentiment analysis, word embedding model, Word2Vec
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-232627
Additional Information:

Urspr. Konferenzveröffentlichung/Originally conference publication: 1st Annual Conference of Computational Literary Studies, 01.-02.06.2022, Darmstadt, Germany

Classification DDC: 800 Literature > 800 Literature, rhetoric and criticism
Divisions: 02 Department of History and Social Science > Institut für Sprach- und Literaturwissenschaft > Digital Philology – Modern German Literary Studies
Date Deposited: 21 Feb 2023 10:36
Last Modified: 23 Feb 2023 08:57
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23262
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