Environmental, Social and Governance Sentiment Analysis on Slovene News: A Novel Dataset and Models

• URL: http://arxiv.org/abs/2604.06826v1
• Date: Wed, 08 Apr 2026 08:42:02 GMT
• Title: Environmental, Social and Governance Sentiment Analysis on Slovene News: A Novel Dataset and Models
• Abstract summary: We introduce the first publicly available Slovene ESG sentiment dataset and a suite of models for automatic ESG sentiment detection.<n>The dataset, derived from the MaCoCu Slovene news collection, combines large language model (LLM)-assisted filtering with human annotation of company-related ESG content.
• Score: 7.1882115136129245
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Environmental, Social, and Governance (ESG) considerations are increasingly integral to assessing corporate performance, reputation, and long-term sustainability. Yet, reliable ESG ratings remain limited for smaller companies and emerging markets. We introduce the first publicly available Slovene ESG sentiment dataset and a suite of models for automatic ESG sentiment detection. The dataset, derived from the MaCoCu Slovene news collection, combines large language model (LLM)-assisted filtering with human annotation of company-related ESG content. We evaluate the performance of monolingual (SloBERTa) and multilingual (XLM-R) models, embedding-based classifiers (TabPFN), hierarchical ensemble architectures, and large language models. Results show that LLMs achieve the strongest performance on Environmental (Gemma3-27B, F1-macro: 0.61) and Social aspects (gpt-oss 20B, F1-macro: 0.45), while fine-tuned SloBERTa is the best model on Governance classification (F1-macro: 0.54). We then show in a small case study how the best-preforming classifier (gpt-oss) can be applied to investigate ESG aspects for selected companies across a long time frame.

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