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Tagset zur Lyrikanalyse: Gattungen, Emotionen, Emotionsmarker

Authors: Merten Kröncke;

Tagset zur Lyrikanalyse: Gattungen, Emotionen, Emotionsmarker

Abstract

Das Poster präsentiert ein Tagset zur Analyse und Annotation von Gattungen, Emotionen und Emotionsmarkern in lyrischen Texten. Mit dem Tagset lassen sich zum Beispiel Fragen folgender Art untersuchen: Welche Emotionen werden in den annotierten Texten am häufigsten gestaltet? Welche Gattungen kommen am häufigsten vor? Gibt es Emotionen, die in bestimmten Gattungen über- und in anderen Gattungen unterrepräsentiert sind? Hängen manche Emotionen besonders eng mit bestimmten Typen von Emotionsmarkern zusammen? Das Tagset erlaubt in puncto Gattungen die Angabe einer oder mehrerer thematischer Gattungen (Naturlyrik, Liebeslyrik usw.) und nicht-thematischer Gattungen (Ballade, Sonett usw.) sowie die Annotation der situativen Bestimmtheit. Für die Analyse der Emotionsgestaltung steht eine Liste von ca. 40 diskreten Emotionen zur Verfügung, wobei die Emotionsannotationen zusätzliche Angaben zum Beispiel zur zuschreibenden Instanz oder zur Trägerinstanz der Emotion enthalten können. Ergänzen lässt sich die Emotionsannotation durch die Angabe von vier verschiedenen Emotionsmarker-Typen, etwa Emotionswörtern oder mit Emotionen verknüpften Situationen. Das Tagset wird entwickelt und eingesetzt im Projekt „The Beginnings of Modern Poetry“ innerhalb des DFG-Schwerpunktprogramms „Computational Literary Studies“. DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies" Online: https://gepris.dfg.de/gepris/projekt/402743989 https://dfg-spp-cls.github.io/ Teilprojekt: "The Beginnings of Modern Poetry – Modeling Literary History with Text Similarities" Online: https://gepris.dfg.de/gepris/projekt/424212127 https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Beginnings_of_Poetry

Keywords

Digital Humanities, Tagset, Annotation, Emotions, Modernism, Poetry, Realism, Genre, Computational Literary Studies, SPP 2207

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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