Sentiment Classification of Historical Danish and Norwegian Literary Texts

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Sentiment classification is valuable for literary analysis, as sentiment is crucial in literary narratives. It can, for example, be used to investigate a hypothesis in the literary analysis of 19th-century Scandinavian novels that the writing of female authors in this period was characterized by negative sentiment, as this paper shows. In order to enable a data-driven analysis of this hypothesis, we create a manually annotated dataset of sentence-level sentiment annotations for novels from this period and use it to train and evaluate various sentiment classification methods. We find that pre-trained multilingual language models outperform models trained on modern Danish, as well as classifiers based on lexical resources. Finally, in the classifier-assisted corpus analysis, we both confirm and contest the literary hypothesis and further shed light on the temporal development of the trend. Our dataset and trained models will be useful for future analysis of historical Danish and Norwegian literary texts.
Original languageEnglish
Title of host publicationProceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
PublisherAssociation for Computational Linguistics (ACL)
Publication dateMay 2023
Publication statusPublished - May 2023
EventNoDaLiDa 2023: The 24th Nordic Conference on Computational Linguistics - Faroe Islands, Tórshavn, Denmark
Duration: 22 May 202324 May 2023
Conference number: 24


ConferenceNoDaLiDa 2023
LocationFaroe Islands


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