Text Triplet Extraction Algorithm with Fused Graph Neural Networks and Improved Biaffine Attention Mechanism

Abstract

In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks with an enhanced biaffine attention mechanism. This model amalgamates the sophisticated capabilities of both graph attention and convolutional networks to process graph-structured data, substantially enhancing the interpretation and extraction of textual features. By optimizing the biaffine attention mechanism, the model adeptly uncovers the subtle interplay between aspect terms and emotional expressions, offering enhanced flexibility and superior contextual analysis through dynamic weight distribution. A series of comparative experiments confirm the model’s significant performance improvements across various metrics, underscoring its efficacy and refined effectiveness in ABSA tasks.