@article{ronningstad-etal-2026-entity,
    title = "Entity-Level Sentiment Analysis with Sentence Relevance Detection",
    author = "R{\o}nningstad, Egil  and
      Klinger, Roman  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.638/",
    pages = "8040--8055",
    abstract = "The task of entity-level sentiment analysis (Elsa) is to extract sentiment scores for a given entity (such as person names or organization names) from a text. Elsa is a challenging task and involves processing of longer documents, where several entities may be mentioned with varying importance for the final score aggregation. Fine-tuning encoder-based Transformers (such as BERT) constitutes the state of the art for sentiment predictions, however, these models are still limited by their restricted input lengths. Decoder-only models so far still underperform on the task. We approach the context limitation by learning to extract segments that are relevant for the sentiment prediction for a given entity, without preprocessing by chunking and aggregation. For decoder models, we explore fine-tuning these through supervised fine-tuning and pairwise comparison, a method borrowed from reward modeling for preference optimization. Both methods perform well and set a new standard for the Elsa task. We further show that pairwise classification is faster, simpler, and shows less variance than the more common direct supervision for this task."
}