Fusion of Capsule Networks and Graph Convolution for Dual Channel Aspect-Level Sentiment Analysis
Yanping Liu, Xuefeng Fu, Kailiang Wang, Weikun Chen, Jun Chen · 2024
Most aspect-based sentiment analyses employ dependency trees for extracting text's syntactic structure. Nonetheless, this approach can encounter information loss and data sparsity issues. Therefore, this paper proposes BGCM model for solving these problems. The Bi-LSTM and Global Graph Network are utilized by the model to extract and integrate temporal and structural information from the original text. A Bi-GCN is also employed to fuse multiple features and combine contextual and syntactic structures. To extract deep sentiment features, the model utilizes the Capsule Network. The proposed method in this paper was put to the test using five types of publicly available data, including Twitter, Laptop14, Rest14, Rest15, and Rest16. The results of the experiments indicate that the BGCM model yielded significant improvements in performance compared to the baseline model.