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A Study on the Identification of the Water Army to Improve the Helpfulness of Online Product Reviews

Author

Listed:
  • Chuyang Li

    (School of Economics, Jinan University, Guangzhou 510632, China)

  • Shijia Zhang

    (School of Economics, Jinan University, Guangzhou 510632, China)

  • Xiangdong Liu

    (School of Economics, Jinan University, Guangzhou 510632, China)

Abstract

Based on the perspective of identifying the water army, this paper uses the methods of machine learning and data visualization to analyze the helpfulness of online produce reviews, portray product portraits, and provide real and helpful product reviews. In order to identify and eliminate the water army, the Term Frequency-Inverse Document Frequency Model (TF-IDF) and Latent Semantic Index Model (LSI) are used. After eliminating the water army, three classification methods were selected to perform sentimental analysis, including logistics, SnowNLP, and Convolutional Neural Network for text(TextCNN). The TextCNN has the highest F1 score among the three classification methods. At the same time, the Latent Dirichlet Allocation mode (LDA) is used to extract the topics of various reviews. Finally, targeted countermeasures are proposed to manufacturers, consumers, and regulators.

Suggested Citation

  • Chuyang Li & Shijia Zhang & Xiangdong Liu, 2024. "A Study on the Identification of the Water Army to Improve the Helpfulness of Online Product Reviews," Mathematics, MDPI, vol. 12(20), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3234-:d:1499540
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