An RCG-based study of aspect-level sentiment analysis for hybrid attention

Aspect-based sentiment analysis (ABSA) is a crucial granular task within sentiment analysis, focusing on the precise identification of sentiment orientations for specific aspects within text. Recognizing that identical context words can express opposing sentiment polarities in different situations, it's essential to delve into the nuanced interactions between target and context words. This study introduces an RCG-based Hybrid Attention Network, a novel architecture that adeptly utilizes lexical attention mechanisms to extract lexical features and fortify the relationship between aspects and their corresponding target words. To assess the efficacy of our proposed approach, we conducted experiments on a well-known public dataset. The results show a significant 3.37% enhancement in accuracy and a 1.38% improvement in Macro-F1 scores compared to related methods, affirming the superiority of our technique in enhancing the performance of aspect-level sentiment analysis.