Dual-enhanced graph convolutional networks for aspect-based financial sentiment analysis

Aspect-based financial sentiment analysis (ABFSA) is a challenging task at the intersection of finance and natural language processing, which aims to infer the trend of a specific entity (e.g., a company or stock) by identifying the sentiment reflected in financial-related texts, while there are a great deal of outstanding works dealing with aspect-level sentiment analysis task. However, there remains a deficiency in research explicitly targeting the ABFSA. In particular, due to the expressive differences between texts in the financial domain and social media texts, existing models lose sentiment details when mining the semantics of finance-related texts. To tackle this issue, the paper conducts a thorough analysis of the expressive features of financial texts and proposes a dual-enhanced GCN network (DEGCN) for financial sentiment analysis. DEGCN is composed of two main components: The Sentic-enhanced GCN focuses on fine-grained sentiment connections between words to overcome the loss of sentiment details caused by unified modeling, and the Domain-enhanced GCN is designed based on the characteristics of financial texts, dividing each sentence into finer categories for classification, thereby significantly reducing noise introduced by domain quantification inconsistencies. Extensive experiments and ablation studies on two public benchmark datasets demonstrate that the proposed model achieves significant improvements over previous baselines.