Enhancing Aspect-Based Sentiment Analysis for Portuguese Using Instruction Tuning - Pereira, Gabriel; Barbosa, Luciano; Moreira, Johny; Melo, Tiago; Silva, Altigran (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2024)
This study explores the application of instruction tuning in opensource small language models for Portuguese End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA), focusing on restaurant reviews. Utilizing a diverse dataset from sources such as Google Reviews, TripAdvisor, Instagram, and iFood, the research evaluates the performance of PTT5 Base, a T5 model pretrained on Portuguese data, in comparison to multilingual models, namely FLAN-T5 Base and mT0 Small. The results show that the PTT5 Base has superior capabilities in E2E-ABSA, achieving an F1 Score of 0.60, Precision of 0.61, and Recall of 0.59. These findings emphasize the significance of language-specific pretraining in analyzing customer opinions for the ABSA task.