Multistream BertGCN for Sentiment Classification Based on Cross-Document Learning

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 inf...

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Bibliographic Details
Published in:Quantum engineering 2023-11, Vol.2023, p.1-9
Main Authors: Li, Meng, Xie, Yujin, Yang, Weifeng, Chen, Shenyu
Format: Article
Language:English
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
Citations: Items that this one cites
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Summary: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.
ISSN:2577-0470
2577-0470
DOI:10.1155/2023/3668960