The Moravians are a Christian group that has emerged from a 15th century movement. In this paper, we investigate how memoirs written by the devotees of this group can be analyzed with methods from computational linguistics, in particular sentiment analysis. To this end, we experiment with two different fine-tuning strategies and find that the best performance for ternary sentiment analysis (81% accuracy) is achieved by fine-tuning a German BERT model, outperforming in particular models trained on much larger German sentiment datasets. We further investigate the model(s) using SHAP scores and find that the best performing model struggles with multiple negations and mixed statements. Finally, we show two application scenarios motivated by research questions from religious studies.
@inproceedings{ Brookshire2024aa,
Title = {{Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting}},
Author = { Patrick Brookshire and Nils Reiter },
Booktitle = {{Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)}},
Month = { March },
Year = { 2024 }
}
TY -
TI - Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting
AU - Patrick Brookshire
AU - Nils Reiter
PY - 2024
J2 - Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
ER -