GeoAffect: A Multi-Layer Annotation Schema and Few-Shot LLM Evaluation for Geoaffective Analysis of Literary Texts

Abstract

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.

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