IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v18y2022i1p1-20.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.307904
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    2. Heng Tang & Chang Boon Patrick Lee & Kwee Keong Choong, 0. "Consumer decision support systems for novice buyers – a design science approach," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    3. Michael D. Wang & Jie Lou & Dong Zhang & C. Simon Fan, 2022. "Measuring political and economic uncertainty: a supervised computational linguistic approach," SN Business & Economics, Springer, vol. 2(5), pages 1-17, May.
    4. Xinlu Li & Yuanyuan Lei & Shengwei Ji, 2022. "BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords," Future Internet, MDPI, vol. 14(11), pages 1-15, November.
    5. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    6. Heng Tang & Chang Boon Patrick Lee & Kwee Keong Choong, 2017. "Consumer decision support systems for novice buyers – a design science approach," Information Systems Frontiers, Springer, vol. 19(4), pages 881-897, August.
    7. Jia-Lang Xu & Ying-Lin Hsu, 2022. "The Impact of News Sentiment Indicators on Agricultural Product Prices," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1645-1657, April.
    8. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
    9. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.