TY - GEN

T1 - Sentiment Classification of Historical Danish and Norwegian Literary Texts

AU - Al-Laith, Ali Mohammed Ali

AU - Nielsen Degn, Kirstine

AU - Conroy, Alexander

AU - Pedersen, Bolette Sandford

AU - Bjerring-Hansen, Jens

AU - Hershcovich, Daniel

N1 - Conference code: 24

PY - 2023/5

Y1 - 2023/5

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 324

EP - 334

BT - Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

PB - Association for Computational Linguistics (ACL)

T2 - NoDaLiDa 2023

Y2 - 22 May 2023 through 24 May 2023

ER -