@inproceedings{melo-figueiredo-2026-gender,
    title = "Gender Identification in {B}razilian {P}ortuguese Product Reviews: A Comparative Study of Classical Models, {BERT}, and {LLM}s",
    author = "Melo, Tiago de  and
      Figueiredo, Carlos M. S.",
    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-1.2/",
    pages = "11--19",
    ISBN = "979-8-89176-387-6",
    abstract = "This study analyzes gender identification in Brazilian Portuguese using Amazon reviews drawn from ten product categories. Nine models were evaluated: three classical classifiers (Logistic Regression, Random Forest, and SVM), a multilingual BERT, and five LLMs (ChatGPT 4o, ChatGPT 3.5, DeepSeek, Sabia3, and Sabiazinho). Experiments show that BERT achieved the best performance (macro-F1 = 0.634), outperforming ChatGPT 4o and Logistic Regression by less than one percentage point. Reviews authored by women reach an average F1 of 0.654{---}four points higher than those by men. Performance also varies by domain: books and automotive are easier, whereas baby and pets are more challenging."
}