Benchmarking LLMs for Aspect-Based Sentiment Classification in Slovene Historical Periodicals

Abstract

Historical newspapers present substantial challenges for computational sentiment analysis due to OCR noise, archaic linguistic features, and the absence of domain-specific labeled training data. This paper examines whether instruction-following LLMs can support targeted, mention-level sentiment inference in such conditions. We benchmark four instruction-following LLMs on a manually annotated sample of collective-identity mentions drawn from Slovene historical newspapers. The results provide a benchmark for targeted sentiment classification in OCR-degraded historical Slovene and offer an empirically grounded assessment of the capabilities and limitations of an instruction-tuned LLM in digital humanities research.

Resources

Details