Semantic Parsing for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) faces significant challenges in accurately identifying sentiment polarity for specific aspects within complex sentences, particularly when dealing with implicit sentiments, nested structures, and nuanced semantic relationships. This study introduces the Semantic Parsing Tree (SPT), a novel framework designed to enhance ABSA by addressing these limitations. By integrating advanced attention mechanisms, our approach overcomes the limitations of traditional dependency trees, which often fail to capture the complex semantic relationships crucial for accurate sentiment prediction, particularly in intricate sentence constructs such as nested clauses or implicit sentiments. Converting syntactic trees into SPTs enables our model to preserve and analyze key semantic roles and relationships, facilitating precise sentiment analysis at the aspect level. The integration of SPT with an advanced graph-based attention mechanism, augmented by relational heads, enhances the deep encoding of semantic nuances, significantly improving sentiment analysis accuracy. Comprehensive evaluations across benchmark datasets, including SemEval 2014, Restaurant, and Twitter, indicate that this approach outperforms conventional models in both accuracy and adaptability.