@inproceedings{DodigEtAl2026LREC-FNP,
  title = {Environmental, Social and Governance Sentiment Analysis on Slovene News: A Novel Dataset and Models },
  author = {Dodig, Paula and Koloski, Boshko and Sitar Šuštar, Katarina and Pollak, Senja and Purver, Matthew},
  booktitle = {The 7th Financial Narrative Processing Workshop},
  month = {May},
  year = {2026},
  pages = {49--58},
  address = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor = {El-Haj, Mo and Moreno Sandoval, Antonio and Garcia-Serrano, Ana and Chen, Chung-Chi and Rayson, Paul and Amor Torterolo Orta, Yanco and Martinez, Paloma and Porta, Jordi},
  doi = {10.63317/2j2jz8zyoh4t},
  url = "https://doi.org/10.63317/2j2jz8zyoh4t",
  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.}
}