Aprimorando a Eficiência e a Equidade de uma Abordagem Perspectivista Para Detecção de Ironia - Jesus, Samuel B.; Bianco, Guilherme D.; Junior, Wanderlei; Basile, Valerio; Gonçalves, Marcos André (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), 2025)
Text classification in tasks like hate speech or irony detection is culturally influenced and personally interpretive. Perspectivism, unlike approaches that aggregate opinions by, for instance, majority voting, values specific annotator groups to create fairer models but often involves high computational costs due to fine-tuning of language models. This work explores traditional machine learning methods (SVM, Random Forest, XGBoost) to reduce costs and uses calibration to address inference biases. Results show up to 12 times faster processing without statistical effectiveness loss and improved fairness through reduced bias.