@inproceedings{haglund-bjorklund-2025-opinion,
    title = "Opinion Units: {Concise} and Contextualized Representations for Aspect-Based Sentiment Analysis",
    author = {H{\"a}glund, Emil  and
      Bj{\"o}rklund, Johanna},
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nodalida-1.24/",
    pages = "230--240",
    ISBN = "978-9908-53-109-0",
    abstract = "We introduce opinion units, a contribution to the field Aspect-Based Sentiment Analysis (ABSA) that extends aspect- sentiment pairs by including substantiating excerpts, derived through hybrid abstractive-extractive summarisation. The goal is to provide fine-grained information without sacrificing succinctness and abstraction. Evaluations on review datasets demonstrate that large language models (LLMs) can accurately extract opinion units through few-shot learning. The main types of errors are providing incomplete contexts for opinions and and mischaracterising objective statements as opinions. The method reduces the need for labelled data and allows the LLM to dynamically define aspect types. As a practical evaluation, we present a case study on similarity search across academic datasets and public review data. The results indicate that searches leveraging opinion units are more successful than those relying on traditional data-segmentation strategies, showing robustness across datasets and embeddings."
}