@inproceedings{gonzalez-lopez-etal-2025-applying,
    title = "Applying Transformer Architectures to Detect Cynical Comments in {S}panish Social Media",
    author = "Gonzalez-Lopez, Samuel  and
      Bethard, Steven  and
      Platt-Molina, Rogelio  and
      Orozco, Francisca",
    editor = "Bak, JinYeong  and
      Goot, Rob van der  and
      Jang, Hyeju  and
      Buaphet, Weerayut  and
      Ramponi, Alan  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the Tenth Workshop on Noisy and User-generated Text",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.wnut-1.8/",
    pages = "68--77",
    ISBN = "979-8-89176-232-9",
    abstract = "Detecting cynical comments in online communication poses a significant challenge in human-computer interaction, especially given the massive proliferation of discussions on platforms like YouTube. These comments often include offensive or disruptive patterns, such as sarcasm, negative feelings, specific reasons, and an attitude of being right. To address this problem, we present a web platform for the Spanish language that has been developed and leverages natural language processing and machine learning techniques. The platform detects comments and provides valuable information to users by focusing on analyzing comments. The core models are based on pre-trained architectures, including BETO, SpanBERTa, Multilingual BERT, RoBERTuito, and BERT, enabling robust detection of cynical comments. Our platform was trained and tested with Spanish comments from car analysis channels on YouTube. The results show that models achieve performance above 0.8 F1 for all types of cynical comments in the text classification task but achieve lower performance (around 0.6-0.7 F1) for the more arduous token classification task."
}