A Deep Learning Approach for Aspect Sentiment Triplet Extraction in Portuguese

• José Meléndez Barros USP https://orcid.org/0000-0003-3035-1162
• Glauber De Bona USP https://orcid.org/0000-0002-6795-9887

Resumo

Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence “Large rooms and great breakfast”, ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations, and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese and is competitive with its counterparts in English.

Palavras-chave: Deep learning, Natural Language Processing, Aspect Sentiment Triplet Extraction