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Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering

Author

Listed:
  • Xin Lu

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

  • Donghong Gu

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

  • Haolan Zhang

    (Ningbo Institute of Technology, Zhejiang University, China)

  • Zhengxin Song

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

  • Qianhua Cai

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

  • Hongya Zhao

    (Shenzhen Polytechnic, China)

  • Haiming Wu

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

Abstract

Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.

Suggested Citation

  • Xin Lu & Donghong Gu & Haolan Zhang & Zhengxin Song & Qianhua Cai & Hongya Zhao & Haiming Wu, 2022. "Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 18(1), pages 1-20, January.
    • Handle: RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-20
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    References listed on IDEAS

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    1. Lun‐Wei Ku & Hsin‐Hsi Chen, 2007. "Mining opinions from the Web: Beyond relevance retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(12), pages 1838-1850, October.
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    Cited by:

    1. Yunjie Liu & Mu Shengdong & Gu Jijian & Nadia Nedjah, 2022. "Intelligent Prediction of Customer Churn with a Fused Attentional Deep Learning Model," Mathematics, MDPI, vol. 10(24), pages 1-16, December.

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