Dual-Graph Convolutional Network with Context Fusion for Chinese Sentiment Classification
Existing research on sentiment classification primarily focuses on textual contextual semantics while neglecting word co-occurrence and syntactic structure information. It also emphasizes semantic analysis of sequential structures but overlooks non-linear structural semantics. To address these limitations, we propose the DGCNCF (Dual-Graph Convolutional Network with Context Fusion) model for sentiment classification. The model employs a BiLSTM with an attention mechanism to extract textual contextual information. Subsequently, a text co-occurrence graph and a syntactic dependency graph are constructed separately. Meanwhile, contextual information is fused with node information within these graphs to obtain graph embedding representations of the text. Then, the model performs representation learning on the two graphs using GCN, thereby capturing the global word co-occurrence features and global syntactic dependency structure characteristics of the text. By integrating textual contextual information, word co-occurrence information, and syntactic dependency structure information, the model captures text features from semantic, lexical, and syntactic perspectives, thereby mitigating the limitations of relying on single-dimensional features. The validity of the model is verified on two public datasets, and the experimental results demonstrate that the model achieves effective sentiment classification performance.