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Validating Topic Modeling as a Method of Analyzing Sujet and Theme

Schröter, Julian ; Du, Keli (2023)
Validating Topic Modeling as a Method of Analyzing Sujet and Theme.
In: Journal of Computational Literary Studies, 2022, 1 (1)
doi: 10.26083/tuprints-00023256
Artikel, Zweitveröffentlichung, Verlagsversion

Kurzbeschreibung (Abstract)

In Computational Literary Studies (CLS), several procedures for thematic analysis have been adapted from NLP and Computer Science. Among these procedures, topic modeling is the most prominent and popular technique. We maintain, however, that this procedure is used only in the context of exploration up to date, but not in the context of justification. When we seek to prove assumptions concerning the correlation between genres, methods of computational text analysis have to be set up in research environments of justification, i.e. in environments of hypothesis testing. We provide a holistic model of validation and conceptual disambiguation of the notion of aboutness as sujet, fabula, and theme, and discuss essential methodological requirements for hypothesis-based analysis. As we maintain that validation has to be performed for individual tasks respectively, we shall perform empirical validation of topic modeling based on a new corpus of German novellas and comprehensive annotations and draw hypothetical generalizations on the applicability of topic modeling for analyzing aboutness in the domain of narrative fiction.

Typ des Eintrags: Artikel
Erschienen: 2023
Autor(en): Schröter, Julian ; Du, Keli
Art des Eintrags: Zweitveröffentlichung
Titel: Validating Topic Modeling as a Method of Analyzing Sujet and Theme
Sprache: Englisch
Publikationsjahr: 2023
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2022
Verlag: Universitäts- und Landesbibliothek Darmstadt
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Journal of Computational Literary Studies
Jahrgang/Volume einer Zeitschrift: 1
(Heft-)Nummer: 1
Kollation: 20 Seiten
DOI: 10.26083/tuprints-00023256
URL / URN: https://tuprints.ulb.tu-darmstadt.de/23256
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Herkunft: Zweitveröffentlichung von TUjournals
Kurzbeschreibung (Abstract):

In Computational Literary Studies (CLS), several procedures for thematic analysis have been adapted from NLP and Computer Science. Among these procedures, topic modeling is the most prominent and popular technique. We maintain, however, that this procedure is used only in the context of exploration up to date, but not in the context of justification. When we seek to prove assumptions concerning the correlation between genres, methods of computational text analysis have to be set up in research environments of justification, i.e. in environments of hypothesis testing. We provide a holistic model of validation and conceptual disambiguation of the notion of aboutness as sujet, fabula, and theme, and discuss essential methodological requirements for hypothesis-based analysis. As we maintain that validation has to be performed for individual tasks respectively, we shall perform empirical validation of topic modeling based on a new corpus of German novellas and comprehensive annotations and draw hypothetical generalizations on the applicability of topic modeling for analyzing aboutness in the domain of narrative fiction.

Freie Schlagworte: sujet, theme, validation, topic modeling, content
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-232568
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Urspr. Konferenzveröffentlichung/Originally conference publication: 1st Annual Conference of Computational Literary Studies, 01.-02.06.2022, Darmstadt, Germany

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 800 Literatur > 800 Literatur, Rhetorik, Literaturwissenschaft
Fachbereich(e)/-gebiet(e): 02 Fachbereich Gesellschafts- und Geschichtswissenschaften > Institut für Sprach- und Literaturwissenschaft > Digital Philology - Neuere deutsche Literaturwissenschaft
02 Fachbereich Gesellschafts- und Geschichtswissenschaften
02 Fachbereich Gesellschafts- und Geschichtswissenschaften > Institut für Sprach- und Literaturwissenschaft
Hinterlegungsdatum: 21 Feb 2023 10:23
Letzte Änderung: 27 Feb 2023 12:46
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