Exploring aspect-based sentiment analysis methodologies for literary-historical research purposes
- Publication type
- C1
- Publication status
- Published
- Authors
- Dejaeghere, T., Singh, P., Lefever, E., & Birkholz, J.
- Editor
- Rachele Sprugnoli and Marco Passarotti
- Series
- Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
- Pagination
- 129-143
- Publisher
- ELRA
- Conference
- Third Workshop on Language Technologies for Historical and Ancient Languages @ LREC-COLING-2024 (LT4HALA 2024) (Turin, Italy)
- Download
- (.pdf)
- View in Biblio
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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.