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.