@inproceedings{brookshire-reiter-2024-modeling,
title = "Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting",
author = "Brookshire, Patrick and
Reiter, Nils",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.10",
pages = "91--100",
abstract = "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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="brookshire-reiter-2024-modeling">
<titleInfo>
<title>Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting</title>
</titleInfo>
<name type="personal">
<namePart type="given">Patrick</namePart>
<namePart type="family">Brookshire</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nils</namePart>
<namePart type="family">Reiter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yuri</namePart>
<namePart type="family">Bizzoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stefania</namePart>
<namePart type="family">Degaetano-Ortlieb</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="family">Kazantseva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stan</namePart>
<namePart type="family">Szpakowicz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julians, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>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.</abstract>
<identifier type="citekey">brookshire-reiter-2024-modeling</identifier>
<location>
<url>https://aclanthology.org/2024.latechclfl-1.10</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>91</start>
<end>100</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting
%A Brookshire, Patrick
%A Reiter, Nils
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Szpakowicz, Stan
%S Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F brookshire-reiter-2024-modeling
%X 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.
%U https://aclanthology.org/2024.latechclfl-1.10
%P 91-100
Markdown (Informal)
[Modeling Moravian Memoirs: Ternary Sentiment Analysis in a Low Resource Setting](https://aclanthology.org/2024.latechclfl-1.10) (Brookshire & Reiter, LaTeCHCLfL-WS 2024)
ACL