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| Unbalanced Fake Review Processing Model Based on Cost-Sensitive Learning |
Liu Meiling1( ),Shang Yue1,Zhao Tiejun2,Zhou Jiyun3 |
1School of Information and Computer Engineering, Northeast Forestry University, Harbin 150006, China
2School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
3Lieber Institute, Johns Hopkins University, Baltimore, MD 21218, USA |
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Abstract [Objective] This study aims to enhance the detection of fake reviews by improving the model’s ability to learn deep semantic information from text and addressing the problem of data imbalance. [Methods] User behavior and text characteristics of the dataset were analyzed to automatically calculate a cost-sensitive matrix based on inter-class separability, thereby improving the model’s ability to learn from unbalanced data. Additionally, the text encoding ability of BERT was utilized to optimize the model further. [Results] Extensive experiments on the YelpCHI dataset showed that the proposed model outperformed existing advanced methods with an 18% improvement in F1 value and a 12% improvement in AUC value. [Limitations] While the proposed method has achieved promising results, further research is needed to explore its applicability to other domains. [Conclusions] Leveraging user behavior and text features for category separability calculation effectively enhances the performance of the model in detecting fake reviews. The proposed method’s integration of cost-sensitive matrix and BERT’s text encoding ability holds great potential for improving the detection of fake reviews.
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Received: 06 May 2022
Published: 09 August 2023
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| Fund:Natural Science Foundation of Heilongjiang Province(LH2022F002);National Natural Science Foundation of China(61702091) |
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Corresponding Authors:
Liu Meiling，ORCID：0000-0003-4208-7274，E-mail： mlliu@nefu.edu.cn。
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