基于对比学习的多视角特征融合方面级情感分析模型

doi:10.16451/j.cnki.issn1003-6059.202410004

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Abstract Current aspect-based sentiment analysis methods typically extract sentiment features through dependency tree and attention mechanism. These methods are susceptible to noise from irrelevant contextual information and often neglect to model the global sentiment features of sentences, making it difficult to process sentences that implicitly express sentiment. To address these problems, a contrastive learning based multi-view feature fusion model for aspect-based sentiment analysis(CLMVFF) is proposed. First, graph convolutional networks are utilized to encode information in dependency graph, constituent graph and semantic graph. The global sentiment node is constructed in each graph to learn global sentiment features while introducing external knowledge embedding to enrich sentiment features. Second, contrastive learning is exploited to mitigate the negative influence of noise. Combined with the similarity separation, the sentiment features are enhanced. Finally, the dependency graph representation, constituent graph representation, semantic graph representation and external knowledge embeddings are fused. Experimental results on three datasets demonstrate that CLMVFF achieves the improvement of performance.

Key words： Aspect-Based Sentiment Analysis Contrastive Learning Graph Convolutional Networks Attention Mechanism

Received: 10 September 2024

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61972182)

Corresponding Authors: XIA Hongbin, Ph.D., professor. His research interests include personalized recommendation and natural language processing.

About author:: WU Xing, Master student. His research interests include natural language processing and deep learning. LIU Yuan, Ph.D., professor. His research interests include network security and social network.

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Cite this article:

WU Xing,XIA Hongbin,LIU Yuan. Contrastive Learning Based Multi-view Feature Fusion Model for Aspect-Based Sentiment Analysis[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(10): 910-922.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202410004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I10/910

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