A Data Augmentation and Validation Pipeline for Improving Emotion Classification in Mobile App Reviews - Oliveira, Kalidsa B. de; Lunardi, Gabriel M.; Silva, Williamsom; Silveira, Thiago L. T. da; Oliveira, Adriano Q. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de Dados, 2025)
This paper examines GPT-2 based data augmentation to improve sentiment classification of Portuguese mobile app reviews, employing the BERTimbau model. A pipeline is proposed, integrating synthetic data generation with semantic analysis, UMAP for dimensionality reduction, and HDBSCAN for clustering and validation. Results show validated and balanced synthetic augmentation boosts model performance in sparse or imbalanced data scenarios.