Creation of the Estonian Subjectivity Dataset: Assessing the Degree of Subjectivity on a Scale
This provides a resource for subjectivity analysis in Estonian, but it is incremental as it adapts existing methods to a new language.
The researchers created an Estonian-language dataset of 1,000 documents rated for subjectivity on a 0-100 scale by four annotators, with moderate inter-annotator correlations that improved after re-annotation, and found that GPT-5 scores were similar to humans but not fully interchangeable.
This article presents the creation of an Estonian-language dataset for document-level subjectivity, analyzes the resulting annotations, and reports an initial experiment of automatic subjectivity analysis using a large language model (LLM). The dataset comprises of 1,000 documents-300 journalistic articles and 700 randomly selected web texts-each rated for subjectivity on a continuous scale from 0 (fully objective) to 100 (fully subjective) by four annotators. As the inter-annotator correlations were moderate, with some texts receiving scores at the opposite ends of the scale, a subset of texts with the most divergent scores was re-annotated, with the inter-annotator correlation improving. In addition to human annotations, the dataset includes scores generated by GPT-5 as an experiment on annotation automation. These scores were similar to human annotators, however several differences emerged, suggesting that while LLM based automatic subjectivity scoring is feasible, it is not an interchangeable alternative to human annotation, and its suitability depends on the intended application.