Fine-Grained Sentiment Analysis Based on SSFF-GCN Model

The research on aspect-based sentiment analysis (ABSA) mostly relies on a single attention mechanism or grammatical semantic information, which makes it less effective in dealing with complex language structures. To address the challenges in fine-grained sentiment analysis tasks, this paper establishes a novel model of syntax and semantics based on feature fusion together with a graph convolutional network (SSFF-GCN), which includes a dual-channel information extraction layer by combining syntactic dependency graphs and semantic information, and consists of three important modules: the syntactic feature enhancement module, semantic feature extraction module, and feature fusion module. In the grammar feature enhancement module, this model uses dependency trees to capture the structural relationship between emotional words and target words and adds a dual affine attention module to enhance grammar learning ability. In the semantic feature extraction module, aspect-aware attention combined with self-attention is used to extract semantic associations in sentences, which ensures effective capture of long-distance dependency information. The feature fusion module dynamically combines the enhanced syntactic and semantic information through a gated mechanism; therefore, it enhances the model’s ability to express emotional features. The empirical results show that the SSFF-GCN model is generally superior to existing models on several publicly available datasets.