@inproceedings{campos-etal-2026-structured,
    title = "Structured Sentiment Analysis in {B}razilian {P}ortuguese: An Exploratory Study Using {BERT}imbau",
    author = "Campos, Andrew B.  and
      Corr{\^e}a, Ulisses B.  and
      Freitas, Larissa A. de",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.92/",
    pages = "927--932",
    ISBN = "979-8-89176-387-6",
    abstract = "Structured Sentiment Analysis (SSA) aims to extract fine-grained opinion structures as tuples (holder, target, expression, polarity). While recent advances have improved SSA for English, Brazilian Portuguese lacks dedicated resources. This paper presents an exploratory study introducing a manually annotated dataset of hotel reviews for SSA in Brazilian Portuguese. We propose a baseline approach fine-tuning the BERTimbau model under a BIO tagging scheme to extract sentiment spans. Unlike traditional approaches that model relations explicitly, we assess the viability of span-level extraction as a first step for SSA in this language. Experimental results using a strict train/validation/test split show that our approach achieves a span-level F1-score of 48.41 for holder extraction and a macro F1-score of 61.52. We also discuss the linguistic challenges of holder extraction in Portuguese, specifically regarding implicit subjects (pro-drop), and provide a detailed error analysis. These results establish a preliminary baseline for future relation-aware models in Portuguese."
}