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    <abstract>This study explores aspect-based sentiment analysis (ABSA) methodologies for literary-historical research, aiming to address the limitations of traditional sentiment analysis in understanding nuanced aspects of literature. It evaluates three ABSA toolchains: rule-based, machine learning-based (utilizing BERT and MacBERTh embeddings), and a prompt-based workflow with Mixtral 8x7B. Findings highlight challenges and potentials of ABSA for literary-historical analysis, emphasizing the need for context-aware annotation strategies and technical skills. The research contributes by curating a multilingual corpus of travelogues, publishing an annotated dataset for ABSA, creating openly available Jupyter Notebooks with Python code for each modeling approach, conducting pilot experiments on literary-historical texts, and proposing future endeavors to advance ABSA methodologies in this domain.</abstract>
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%0 Conference Proceedings
%T Exploring Aspect-Based Sentiment Analysis Methodologies for Literary-Historical Research Purposes
%A Dejaeghere, Tess
%A Singh, Pranaydeep
%A Lefever, Els
%A Birkholz, Julie
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F dejaeghere-etal-2024-exploring
%X This study explores aspect-based sentiment analysis (ABSA) methodologies for literary-historical research, aiming to address the limitations of traditional sentiment analysis in understanding nuanced aspects of literature. It evaluates three ABSA toolchains: rule-based, machine learning-based (utilizing BERT and MacBERTh embeddings), and a prompt-based workflow with Mixtral 8x7B. Findings highlight challenges and potentials of ABSA for literary-historical analysis, emphasizing the need for context-aware annotation strategies and technical skills. The research contributes by curating a multilingual corpus of travelogues, publishing an annotated dataset for ABSA, creating openly available Jupyter Notebooks with Python code for each modeling approach, conducting pilot experiments on literary-historical texts, and proposing future endeavors to advance ABSA methodologies in this domain.
%U https://aclanthology.org/2024.lt4hala-1.16
%P 129-143

Markdown (Informal)

[Exploring Aspect-Based Sentiment Analysis Methodologies for Literary-Historical Research Purposes](https://aclanthology.org/2024.lt4hala-1.16) (Dejaeghere et al., LT4HALA-WS 2024)

• Exploring Aspect-Based Sentiment Analysis Methodologies for Literary-Historical Research Purposes (Dejaeghere et al., LT4HALA-WS 2024)

ACL

• Tess Dejaeghere, Pranaydeep Singh, Els Lefever, and Julie Birkholz. 2024. Exploring Aspect-Based Sentiment Analysis Methodologies for Literary-Historical Research Purposes. In Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024, pages 129–143, Torino, Italia. ELRA and ICCL.