Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation
It is widespread that the consumers browse relevant reviews for reference before purchasing the products when online shopping. Some stores or users may write deceptive reviews to mislead consumers into making risky purchase decisions. Existing methods of deceptive review detection did not consider t...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/5321280 |
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| _version_ | 1832550719166611456 |
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| author | Ling Yuan Dan Li Shikang Wei Mingli Wang |
| author_facet | Ling Yuan Dan Li Shikang Wei Mingli Wang |
| author_sort | Ling Yuan |
| collection | DOAJ |
| description | It is widespread that the consumers browse relevant reviews for reference before purchasing the products when online shopping. Some stores or users may write deceptive reviews to mislead consumers into making risky purchase decisions. Existing methods of deceptive review detection did not consider the valid product review sets and classification probability of feature weights. In this research, we propose a deceptive review detection algorithm based on the target product identification and the calculation of the Metapath feature weight, noted as TM-DRD. The review dataset of target product is modeled as a heterogeneous review information network with the feature nodes. The classification method of graph is used to detect the deceptive reviews, which can improve the efficiency and accuracy of deceptive review detection due to the sparsity, imbalance of deceptive reviews, and the absence of category probability of feature weight calculation. The TM-DRD algorithm we proposed is validated on the real review dataset Yelp and compared with the SpEagle, NFC, and NetSpam algorithm. The experiment results demonstrate that the TM-DRD algorithm performs better than the other method with regard to the accuracy and efficiency. |
| format | Article |
| id | doaj-art-b19e5041d15f465fa414f917d71eeb4c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b19e5041d15f465fa414f917d71eeb4c2025-02-03T06:05:58ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/53212805321280Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight CalculationLing Yuan0Dan Li1Shikang Wei2Mingli Wang3School of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, ChinaIt is widespread that the consumers browse relevant reviews for reference before purchasing the products when online shopping. Some stores or users may write deceptive reviews to mislead consumers into making risky purchase decisions. Existing methods of deceptive review detection did not consider the valid product review sets and classification probability of feature weights. In this research, we propose a deceptive review detection algorithm based on the target product identification and the calculation of the Metapath feature weight, noted as TM-DRD. The review dataset of target product is modeled as a heterogeneous review information network with the feature nodes. The classification method of graph is used to detect the deceptive reviews, which can improve the efficiency and accuracy of deceptive review detection due to the sparsity, imbalance of deceptive reviews, and the absence of category probability of feature weight calculation. The TM-DRD algorithm we proposed is validated on the real review dataset Yelp and compared with the SpEagle, NFC, and NetSpam algorithm. The experiment results demonstrate that the TM-DRD algorithm performs better than the other method with regard to the accuracy and efficiency.http://dx.doi.org/10.1155/2018/5321280 |
| spellingShingle | Ling Yuan Dan Li Shikang Wei Mingli Wang Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation Complexity |
| title | Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation |
| title_full | Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation |
| title_fullStr | Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation |
| title_full_unstemmed | Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation |
| title_short | Research of Deceptive Review Detection Based on Target Product Identification and Metapath Feature Weight Calculation |
| title_sort | research of deceptive review detection based on target product identification and metapath feature weight calculation |
| url | http://dx.doi.org/10.1155/2018/5321280 |
| work_keys_str_mv | AT lingyuan researchofdeceptivereviewdetectionbasedontargetproductidentificationandmetapathfeatureweightcalculation AT danli researchofdeceptivereviewdetectionbasedontargetproductidentificationandmetapathfeatureweightcalculation AT shikangwei researchofdeceptivereviewdetectionbasedontargetproductidentificationandmetapathfeatureweightcalculation AT mingliwang researchofdeceptivereviewdetectionbasedontargetproductidentificationandmetapathfeatureweightcalculation |