Creation of the Estonian Subjectivity Dataset: Assessing the Degree of Subjectivity on a Scale
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Computer Science > Computation and Language
arXiv:2512.09634 (cs)
[Submitted on 10 Dec 2025 (v1), last revised 5 Jun 2026 (this version, v2)]
Title:Creation of the Estonian Subjectivity Dataset: Assessing the Degree of Subjectivity on a Scale
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Abstract: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.
| Comments: | 9 pages, 5 figures, 3 appendixes, LREC 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2512.09634 [cs.CL] |
| (or arXiv:2512.09634v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2512.09634
arXiv-issued DOI via DataCite
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| Journal reference: | Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026) 8204-8216 |
| Related DOI: | https://doi.org/10.63317/35rspcvi32vp
DOI(s) linking to related resources
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Submission history
From: Karl Gustav Gailit [view email][v1] Wed, 10 Dec 2025 13:22:16 UTC (180 KB)
[v2] Fri, 5 Jun 2026 00:50:17 UTC (181 KB)
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