Benchmarking LLMs for Aspect-Based Sentiment Classification in Slovene Historical Periodicals
Year: 2026
Type: conference-paper
Abstract: Historical newspapers present substantial challenges for computational sentiment analysis due to OCR errors, archaic linguistic features, and the absence of domain-specific labelled training data.This paper investigates whether instruction-following LLMs can facilitate aspect-level sentiment inference under 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.
Institutions: University of Ljubljana, Institute of Contemporary History
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166523245