Multistream BertGCN for Sentiment Classification Based on Cross-Document Learning

• Published in: Quantum engineering Vol. 2023; pp. 1 - 9
• Main Authors: Li, Meng, Xie, Yujin, Yang, Weifeng, Chen, Shenyu
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 13.11.2023; John Wiley & Sons, Inc
• Subjects: Accuracy; Artificial neural networks; Classification; Datasets; Deep learning; Dictionaries; Documents; Experiments; Graph representations; Graphs; Information processing; Language; Learning; Machine learning; Natural language processing; Neural networks; Public opinion surveys; Sentiment analysis; Text categorization; Topology
• ISSN: 2577-0470; 2577-0470
• DOI: 10.1155/2023/3668960

Very recently, the BERT graph convolutional network (BertGCN) model has attracted much attention from researchers due to its good text classification performance. However, just using original documents in the corpus to construct the topology of graphs for GCN-based models may lose some effective information. In this paper, we focus on sentiment classification, an important branch of text classification, and propose the multistream BERT graph convolutional network (MS-BertGCN) for sentiment classification based on cross-document learning. In the proposed method, we first combine the documents in the training set based on within-class similarity. Then, each heterogeneous graph is constructed using a group of combinations of documents for the single-stream BertGCN model. Finally, we construct multistream-BertGCN (MS-BertGCN) based on multiple heterogeneous graphs constructed from different groups of combined documents. The experimental results show that our MS-BertGCN model outperforms state-of-the-art methods on sentiment classification tasks.