Graph-enhanced implicit aspect-level sentiment analysis based on multi-prompt fusion

Implicit Aspect-Level Sentiment Analysis aims to identify aspect items and opinion items that do not appear in unstructured text. These items can be analyzed based on the semantics of the text. The analysis also seeks to determine the sentiment tendency of each aspect in the evaluated sentence. The difficulty of semantic comprehension is greatly increased by the fact that the evaluation texts include omitted expressions. Additionally, they often use short texts with few available features, making the analysis more challenging. To this end, this paper proposes a generative model for graph-enhanced implicit aspect-level sentiment analysis based on multi-prompt fusion. The generative pre-training model T5 is used in combination with graph neural networks to capture prompts and semantic information in the context, enabling the understanding of implicit emotions. By employing multi-prompt fusion, the model fully leverages the complementary strengths of multiple prompts, avoiding the incompleteness and instability associated with using a single prompt. In addition, to optimize model performance, this paper designs aspect term and opinion term recognition as index generation tasks. It also proposes a differentiated loss function with varying penalties for different types of errors. Compared to the existing state-of-the-art models, the proposed model improves the F1 value by 1.99% on the standard dataset restaurantACOS. Additionally, it achieves a 1.83% improvement on the LaptopACOS dataset. Ablation experiments demonstrate that each improvement is effective for aspect-level sentiment analysis. These improvements also lead to better results in low-resource settings.