Enhancing Aspect-Based Sentiment Analysis for Portuguese Using Instruction Tuning

  • Gabriel Pereira UFPE
  • Luciano Barbosa UFPE
  • Johny Moreira UFAM
  • Tiago Melo UEA
  • Altigran Silva UFAM

Resumo


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.

Palavras-chave: Generative AI, Small Language Models, Natural Language Processing, Transformers

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Publicado
17/11/2024
PEREIRA, Gabriel; BARBOSA, Luciano; MOREIRA, Johny; MELO, Tiago; SILVA, Altigran. Enhancing Aspect-Based Sentiment Analysis for Portuguese Using Instruction Tuning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 990-1001. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2024.245109.