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        <title>Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media</title>
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        <namePart type="given">Samuel</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media
%A Gonzalez-Lopez, Samuel
%A Bethard, Steven
%A Platt-Molina, Rogelio
%A Orozco, Francisca
%Y Bak, JinYeong
%Y Goot, Rob van der
%Y Jang, Hyeju
%Y Buaphet, Weerayut
%Y Ramponi, Alan
%Y Xu, Wei
%Y Ritter, Alan
%S Proceedings of the Tenth Workshop on Noisy and User-generated Text
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-232-9
%F gonzalez-lopez-etal-2025-applying
%X 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.
%R 10.18653/v1/2025.wnut-1.8
%U https://aclanthology.org/2025.wnut-1.8/
%U https://doi.org/10.18653/v1/2025.wnut-1.8
%P 68-77

Markdown (Informal)

[Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media](https://aclanthology.org/2025.wnut-1.8/) (Gonzalez-Lopez et al., WNUT 2025)

• Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media (Gonzalez-Lopez et al., WNUT 2025)

ACL

• Samuel Gonzalez-Lopez, Steven Bethard, Rogelio Platt-Molina, and Francisca Orozco. 2025. Applying Transformer Architectures to Detect Cynical Comments in Spanish Social Media. In Proceedings of the Tenth Workshop on Noisy and User-generated Text, pages 68–77, Albuquerque, New Mexico, USA. Association for Computational Linguistics.