Aspect-based sentiment analysis based on multi-granularity graph convolutional network

Aspect sentiment classification (ASC) is a subtask of Aspect-based sentiment analysis (ABSA), and its goal is to predict the sentiment polarity corresponding to specific aspect word within a sentence. Existing ABSA methods have achieved impressive predictive performance by not only leveraging attention mechanism to capture semantic association information within sentences, but also employing graph convolutional network (GCN) to exploit the syntactic structure information of dependency and constituency trees. However, these methods still have two main shortcomings: (1) they overlook the utilization of local sentiment features in the constituency tree, focusing only on global sentiment features; and (2) they fail to comprehensively utilize the syntactic information from both dependency and constituency trees. To address these issues, we propose a novel multi-granularity graph convolutional network (MGCN), which comprises three main components: an attention layer, a mask matrix layer, and a GCN layer. In the attention layer, we constructed semantic association matrices using an attention mechanism to explore the semantic associations between words and overall sentence semantics. In the mask matrix layer, we designed hierarchical rule-based multi-granularity constituency tree mask matrices (CM) to extract sentiment features from local to global levels within the constituency tree. Additionally, to obtain a more comprehensive syntactic features set, we fully fused the structural characteristics of the dependency and constituency trees to create multi-granularity fusion mask matrices (FM), which were further enhanced by the semantic association matrices. Finally, in the GCN layer, we performed convolution operations on the enhanced FM to strengthen the node representations. Experiments on the SemEval 2014 and Twitter datasets demonstrated effectiveness of MGCN.