A Sentiment Analysis Model for Aspect-Based Sentiment Analysis using Biaffine Attention and Sentiment Knowledge Enhancement

Sentiment analysis aims to uncover the sentiment polarity of various targets in text. However, existing models predominantly rely on syntactic structures or sequential information, making it challenging to effectively capture the deep dependencies and complex emotional interactions between multi word aspect and opinion terms. This limitation hampers the accurate modeling of semantic relationships among sentiment triplets—aspect terms, opinion terms, and sentiment polarity. Furthermore, current models often overlook the potential of external sentiment knowledge, which results in suboptimal performance when dealing with complex semantic dependencies and multi-word sentiment relationships. To address these challenges, we propose a novel Aspect-based Sentiment Analysis Model SEBM that leverages bi-affine attention and sentiment knowledge enhancement to improve perfor-mance. First, we introduce a biaffine attention mechanism to model the in-tricate semantic and emotional dependencies between multi-word terms, en-abling more precise capture of emotional interactions and semantic relation-ships. Second, we integrate external sentiment knowledge from the Sentic-Net lexicon to optimize the syntactic dependency graph, thereby enhancing the emotional dependencies between the context and aspect terms. This ap-proach compensates for the limitations of existing models in sentiment in-formation modeling. We validate the proposed method on three publicly available datasets: Restaurant, Laptop, and Twitter. The experimental results show that while the accuracy slightly decreased on the Restaurant dataset, SEBM achieved improvements of 2.0 and 1.18 in accuracy on the Laptop and Twitter datasets, respectively. Moreover, SEBM outperformed the base-line model SSEGCN in Macro-F1 scores, with improvements of 0.69 , 2.78 , and 1.01 on the three datasets.