Estonian Subjectivity Dataset Created for Document-Level Analysis Using Human Annotators and LLM Scoring
Researchers created a 1,000-document Estonian-language dataset in which texts are rated for subjectivity on a 0–100 scale by human annotators and GPT-5. The dataset covers journalistic articles and web texts, with a re-annotation step applied to the most contested items to improve inter-annotator agreement. The work highlights both the promise and the limits of using large language models as substitutes for human judgment in subjectivity analysis.
A team of researchers developed an Estonian-language subjectivity dataset comprising 300 journalistic articles and 700 randomly selected web texts, each rated on a continuous scale from 0 (fully objective) to 100 (fully subjective) by four human annotators. Inter-annotator correlations were moderate overall, and a subset of texts with the most divergent scores was re-annotated, which improved agreement. Alongside human labels, GPT-5 was used to generate subjectivity scores as an experiment in annotation automation; its scores were broadly similar to human ratings but showed notable differences in specific cases. The authors conclude that LLM-based subjectivity scoring is feasible but not interchangeable with human annotation, and that its appropriateness depends on the intended downstream application. The dataset and findings were presented at the Fifteenth Language Resources and Evaluation Conference (LREC 2026), contributing a low-resource language resource to the field of computational linguistics.
What's missing
The study focuses on document-level subjectivity and does not address sentence- or phrase-level granularity, which may limit applicability to certain NLP tasks.
What different sources said
- arXiv cs.CLCenter
Creation of the Estonian Subjectivity Dataset: Assessing the Degree of Subjectivity on a Scale
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