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

Chuyang Li, Shijia Zhang and Xiangdong Liu ()
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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

Mathematics, 2024, vol. 12, issue 20, 1-13

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.

Keywords: online product reviews; water army; latent semantic index; latent dirichlet allocation; convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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