Anomaly Detection in Text Data: A Semi-Supervised Approach Applied to the Portuguese Domain - Maia, Fabio Masaracchia; Costa, Anna Helena Reali (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2024: Anais do XV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, 2024)
Anomaly detection, driven by advancements in machine learning and deep learning, has gained significant importance across various fields. However, its application to unstructured textual data, particularly in Portuguese, remains underexplored. In textual analysis, these techniques are crucial for detecting deviations within text collections. This paper investigates state-of-the-art methods for anomaly detection in Portuguese text corpora and introduces a new, flexible loss function designed to enhance detection across different contamination levels. By evaluating these methods on benchmark datasets, specifically in the contexts of hate speech detection and sentiment analysis, we address existing challenges and contribute to the development of more effective anomaly detection techniques for Portuguese text data.