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Triplet Contrastive Learning for Aspect Level Sentiment Classification

Author

Listed:
  • Haoliang Xiong

    (School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
    These authors contributed equally to this work.)

  • Zehao Yan

    (School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
    These authors contributed equally to this work.)

  • Hongya Zhao

    (Industrial Center, Shenzhen Polytechnic, Shenzhen 518055, China)

  • Zhenhua Huang

    (School of Computer Science, South China Normal University, Guangzhou 510631, China)

  • Yun Xue

    (School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China)

Abstract

The domain of Aspect Level Sentiment Classification, in which the sentiment toward a given aspect is analyzed, attracts much attention in NLP. Recently, the state-of-the-art Aspect Level Sentiment Classification methods are devised by using the Graph Convolutional Networks to deal with both the semantics and the syntax of the sentence. Generally, the parsing of syntactic structure inevitably incorporates irrelevant information toward the aspect. Besides, the syntactic and semantic alignment and uniformity that contribute to the sentiment delivery is currently neglected during processing. In this work, a Triplet Contrastive Learning Network is developed to coordinate the syntactic information and the semantic information. To start with, the aspect-oriented sub-tree is constructed to replace the syntactic adjacency matrix. Further, a sentence-level contrastive learning scheme is proposed to highlight the features of sentiment words. Based on The Triple Contrastive Learning, the syntactic information and the semantic information are thoroughly interacted and coordinated whilst the global semantics and syntax can be exploited. Extensive experiments are performed on three benchmark datasets and achieve accuracies (BERT-based) of 87.40, 82.80, 77.55 on Rest14, Lap14, and Twitter datasets, which demonstrate that our approach achieves state-of-the-art results in Aspect Level Sentiment Classification task.

Suggested Citation

  • Haoliang Xiong & Zehao Yan & Hongya Zhao & Zhenhua Huang & Yun Xue, 2022. "Triplet Contrastive Learning for Aspect Level Sentiment Classification," Mathematics, MDPI, vol. 10(21), pages 1-14, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4099-:d:961892
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    Citations

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    Cited by:

    1. Dehong Zeng & Xiaosong Chen & Zhengxin Song & Yun Xue & Qianhua Cai, 2023. "Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews," Mathematics, MDPI, vol. 11(10), pages 1-16, May.
    2. Jae Hyun Yoon & Jong Won Jung & Seok Bong Yoo, 2024. "Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
    3. Ze Shi & Hongyi Li & Di Zhao & Chengwei Pan, 2023. "Research on Relation Classification Tasks Based on Cybersecurity Text," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    4. Jiehai Chen & Zhixun Qiu & Junxi Liu & Yun Xue & Qianhua Cai, 2023. "Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    5. Weihua Ou & Jianping Gou & Shaoning Zeng & Lan Du, 2023. "Preface to the Special Issue “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics”—Special Issue Book," Mathematics, MDPI, vol. 11(4), pages 1-4, February.

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