Estonian Subjectivity Dataset Created for Document-Level Analysis Using Human Annotators and LLM Scoring
Researchers created an Estonian-language dataset of 1,000 documents rated for subjectivity on a 0-100 scale by human annotators and GPT-5. The dataset includes 300 journalistic articles and 700 web texts, with moderate inter-annotator agreement that improved after re-annotation of divergent cases. The findings suggest LLM-based subjectivity scoring is feasible but not a direct replacement for human annotation.
A new Estonian subjectivity dataset has been developed comprising 1,000 documents—300 journalistic articles and 700 randomly selected web texts—each rated on a continuous scale from fully objective (0) to fully subjective (100) by four human annotators. Initial inter-annotator correlations were moderate, with some texts receiving scores at opposite ends of the scale; however, re-annotation of the most divergent texts improved agreement. The study also tested GPT-5 for automatic subjectivity scoring, finding that LLM-generated scores were similar to human annotations overall. Despite this similarity, notable differences emerged between human and machine scoring, indicating that while LLM-based automation is technically feasible, it remains context-dependent and unsuitable as a direct substitute for human judgment in many applications.
What's missing
The study does not discuss potential limitations regarding the representativeness of the Estonian web texts sample, the specific criteria used to select the 300 journalistic articles, or how subjectivity definitions may vary across different document genres and cultural contexts.
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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