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Evaluation computergestützter Verfahren der Emotionsklassifikation für deutschsprachige Dramen um 1800 ...
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Evaluation computergestützter Verfahren der Emotionsklassifikation für deutschsprachige Dramen um 1800 ...
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Towards a Corpus of Historical German Plays with Emotion Annotations ...
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Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language ...
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Using Deep Learning for Emotion Analysis of 18th and 19th Century German Plays ...
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Using Deep Learning for Emotion Analysis of 18th and 19th Century German Plays
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Abstract:
We present first results of the project “Emotions in Drama” in which we explore the annotation of emotions and the application of computational emotion analysis, predominantly deep learning-based methods, in the context of historical German plays of the time around 1800. We performed a pilot annotation study with five plays generating over 6,500 annotations for up to 13 sub-emotions structured in a hierarchical scheme. This emotion scheme includes common types like joy, anger or hate but also concepts that are specifically important for German literary criticism of this period like friendship, compassion or Schadenfreude. We evaluate the performance of various methods of emotion-based text sequence classification including lexicon-based methods, traditional machine learning, fastText as static word embedding, various transformer models based on BERT- or ELECTRA-architectures and pretrained with contemporary language, transformer-based methods pretrained or finetuned for historical and/or poetic language as well as the finetuning of BERT models via our own corpora and plays. We do achieve state-of-the-art results with hierarchical levels with two or three classes, i. e. the classification of valence (positive/negative). The best models are the transformer-based models gbert-large and gelectra-large by deepset pretrained on large corpora of contemporary German, which achieve accuracy values of up to 83%. Lexicon-based methods, traditional machine learning as well as static word embeddings are consistently outperformed by transformer-based models. Models trained on historical texts show small and inconsistent improvements. The performance becomes significantly smaller for settings with multiple sub-emotions like 6 or 13 due to the general challenge and class imbalances in which the models achieve 57% and 47% respectively. We discuss how we intend to continue our annotations and how to improve the prediction results via various optimization techniques in future work.
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Keyword:
004 Informatik; 400 Sprachwissenschaft; 430 Deutsch; 792 Theater; 800 Literatur; 830 Deutsche Literatur; ddc:004; ddc:400; ddc:430; ddc:792; ddc:800; ddc:830; Linguistik; Literaturwissenschaft; Rhetorik; Tanz
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URL: https://epub.uni-regensburg.de/50827/ https://epub.uni-regensburg.de/50827/1/vDhd-Beitrag-Schmidt-UsingDeepLearning.pdf https://epub.uni-regensburg.de/50827/7/vDhd-Beitrag-Schmidt-UsingDeepLearning-PublishedVersion_workingLinks.pdf https://www.melusinapress.lu/read/10-26298-melusina-8f8w-y749-udlf/section/8d0fefff-384c-4798-b5d7-032809de2430
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Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language
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Towards a Corpus of Historical German Plays with Emotion Annotations
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Sentiment Annotation for Lessing’s Plays: Towards a Language Resource for Sentiment Analysis on German Literary Texts
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„Kann man denn auch nicht lachend sehr ernsthaft sein?" – Zum Einsatz von Sentiment Analyse-Verfahren für die quantitative Untersuchung von Lessings Dramen
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Sentiment Annotation of Historic German Plays: An Empirical Study on Annotation Behavior
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Sentiment Annotation of Historic German Plays: An Empirical Study on Annotation Behavior
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Katharsis – Ein Werkzeug für die quantitative Dramenanalyse ...
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Heterotope Herrschaftsräume in frühhöfischen Epen und ihren Bearbeitungen (König Rother, Herzog Ernst B, D und G)
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Katharsis – Ein Werkzeug für die quantitative Dramenanalyse
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