Comparative Analysis of Text Classification Algorithms - Borges, Beatriz Ribeiro; Faria, Elaine Ribeiro; Gabriel, Paulo H. R. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2025.12276, 2025)
This study presents a comparative analysis of the performance of the text classification task with Transformer-based models (BERT/BERTimbau) in contrast to traditional machine learning algorithms (Decision Tree, XGBoost, Naive Bayes, SVM, MLP) using two textual representations: dense embeddings and TF-IDF. The evaluation was conducted on 5 datasets, 3 binary and 2 multiclass, with texts in Portuguese and English. While Transformers consistently delivered the best overall performance, TF-IDF proved highly competitive–outperforming embeddings and even matching or surpassing BERT in specific cases.