GeoAffect is an annotation framework that has been especially developed to capture how places are emotionally framed in literary narrative. The project focuses on nineteenth-century Greek prose fiction and brings together named entity recognition with an affect schema that distinguishes experiential, appraisal, and identity-oriented relations to place. The annotation design linked entities, emotion spans, and rhetorical devices, allowing us to model not only sentiment but also forms of belonging, alienation, and longing. To test the schema, we created a manually annotated gold dataset of approximately 360 sentences and evaluated thirteen Large Language Models in a few-shot setting for both entity recognition and affect classification. The results indicate that, with carefully designed prompts and selection strategies, LLMs can support structured geoaffective annotation even in low-resource historical language contexts.

How the construction of national consciousness may be captured in the literary production of a whole century? What can the macro-analysis of the 19th-century prose fiction reveal about the formation of the concept of the nation-state of Greece? How could the concept of nationality be detected in literary writing and then interpreted? These are the questions addressed by the research that is published in this paper and which focuses on exploring how the concept of the nation is figured and shaped in 19th-century Greek prose fiction. This paper proposes a methodological approach that combines well-known text mining techniques with computational close reading methods in order to retrieve the nation-related passages and to analyze them linguistically and semantically. The main objective of the paper at hand is to map the frequency and the phraseology of the nation-related references, as well as to explore the phrase patterns in relation to the topic modeling results.