Aspect-Based Sentiment Analysis Using Dual Probability Graph Convolutional Networks (DP-GCN) Integrating Multi-scale Information

Aspect-based sentiment analysis (ABSA) is a fine-grained entity-level sentiment analysis task that aims to identify the emotions associated with specific aspects or details within text. ABSA has been widely applied to various areas such as analyzing product reviews and monitoring public opinion on social media. In recent years, methods based on graph neural networks combined with syntactic information have achieved promising results in the task of ABSA. However, existing methods using syntactic dependency trees contain redundant information, and the relationships with identical weights do not reflect the importance of the aspect words and opinion words’ dependencies. Moreover, ABSA is limited by issues such as short sentence length and informal expression. Therefore, this paper proposes a Double Probabilistic Graph Convolutional Network (DP-GCN) integrating multi-scale information to address the aforementioned issues. Firstly, the original dependency tree is reshaped through pruning, creating aspect-based syntactic dependency tree corresponding syntactic dependency weights. Next, two probability attention matrixes are constructed based on both semantic and syntactic information. The semantic probability attention matrix represents the weighted directed graph of semantic correlations between words. Compared with the discrete adjacency matrix directly constructed by the syntax dependency tree, the probability matrix representing the dependency relationship between words based on syntax information contains rich syntactic information. Based on this, semantic information and syntactic dependency information are separately extracted via graph convolutional networks. Interactive attention is used to guide mutual learning between semantic information and syntactic dependency information, enabling full interaction and fusion of both types of information before finally carrying out sentiment polarity classification. Our model was tested on four public datasets, Restaurant, Laptop, Twitter and MAMS. The accuracy (ACC) and F1 score improved by 0.14% to 1.26% and 0.4% to 2.19%, respectively, indicating its outstanding performance.