Sentiment-Enriched AI for Toxic Speech Detection: A Case Study of Political Discourses in the Valencian Parliament

Antoni Mestre, Franccesco Malafarina, Joan Fons, Manoli Albert, Miriam Gil, Vicente Pelechano

2025

Abstract

The increasing prevalence of toxic speech across various societal domains has raised significant concerns regarding its impact on communication and social interactions. In this context, the analysis of toxicity through AI techniques has gained prominence as a relevant tool for detecting and combating this phenomenon. This study proposes a novel approach to toxic speech detection by integrating sentiment analysis into binary classification models. By establishing a confusion zone for ambiguous probability scores, we direct uncertain cases to a sentiment analysis module that informs final classification decisions. Applied to political discourses in the Valencian Parliament, this sentiment-enriched approach significantly improves classification accuracy and reduces misclassifications compared to traditional methods. These findings underscore the effectiveness of incorporating sentiment analysis to enhance the robustness of toxic speech detection in complex political contexts, paving the way for future research in this relevant area.

Paper Citation

in Harvard Style

Mestre A., Malafarina F., Fons J., Albert M., Gil M. and Pelechano V. (2025). Sentiment-Enriched AI for Toxic Speech Detection: A Case Study of Political Discourses in the Valencian Parliament. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 555-561. DOI: 10.5220/0013159600003890

in Bibtex Style

@conference{icaart25,
author={Antoni Mestre and Franccesco Malafarina and Joan Fons and Manoli Albert and Miriam Gil and Vicente Pelechano},
title={Sentiment-Enriched AI for Toxic Speech Detection: A Case Study of Political Discourses in the Valencian Parliament},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={555-561},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013159600003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Sentiment-Enriched AI for Toxic Speech Detection: A Case Study of Political Discourses in the Valencian Parliament
SN - 978-989-758-737-5
AU - Mestre A.
AU - Malafarina F.
AU - Fons J.
AU - Albert M.
AU - Gil M.
AU - Pelechano V.
PY - 2025
SP - 555
EP - 561
DO - 10.5220/0013159600003890
PB - SciTePress