Authors:
Antoni Mestre
1
;
Franccesco Malafarina
2
;
Joan Fons
1
;
Manoli Albert
1
;
Miriam Gil
3
and
Vicente Pelechano
1
Affiliations:
1
VRAIN Institute, Universitat Politècnica de València, 46022, Valencia, Spain
;
2
Università degli Studi del Sannio, 82100, Benevento, Italy
;
3
Departament d’Informàtica, Universitat de València, 46100, Burjassot, Spain
Keyword(s):
Toxic Speech Detection, Hate Speech, Text Classification, Parliamentary Speeches.
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.
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