Academic Journal of Business & Management, 2024, 6(4); doi: 10.25236/AJBM.2024.060440.
Daming Fan
School of Communication, Qufu Normal University, Rizhao, 276826, China
With the development of e-commerce, many merchants, to lure consumers to buy their own goods, will create the illusion of hot stores and excellent goods by means of false reviews. To identify false reviews, this research paper proposes a false review identification method, which mainly uses entropy weight TOPSIS model, K-Means cluster analysis and logistic regression. The article selects evaluation indexes such as sentiment polarity, text length, review usefulness and rating deviation degree, and calculates the review score by entropy weighting method of TOPSIS model. Subsequently, K-Means clustering was used to categorize the reviews into two groups: customer reviews and machine reviews and validated by logistic regression. The experimental results show that the method has a good ability to recognize false reviews with high accuracy, recall, precision, and AUC value. Taken together, the method provides an effective solution for false comment recognition with practical application potential.
Fake reviews, TOPSIS model, Cluster analysis, Logistic regression
Daming Fan. Research on the identification method of fake reviews in e-commerce. Academic Journal of Business & Management (2024) Vol. 6, Issue 4: 276-281. https://doi.org/10.25236/AJBM.2024.060440.
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