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“Industry manufactured products like electronics, computers, etc. Table 5 Various features of different categories of products

Table 5 “Industry manufactured products like electronics, computers, etc. Table 5 Various features of different categories of products

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Fig. 1 Distribution of Focused Research Works. Review Mining Task takes the percentage of 17%, end-to-end classification task is 22%, and cold-start problems have the same proportion as classification task. Spammer detection task takes the percentage of 39%
Fig. 1 Distribution of Focused Research Works. Review Mining Task takes the percentage of 17%, end-to-end classification task is 22%, and cold-start problems have the same proportion as classification task. Spammer detection task takes the percentage of 39%
Table 1 Description of features in CATS Multicriteria Decision: Viviani et al. [20] used a multicrite- ria choice making approach based both on the evaluation of numerous criteria and the utilize of accumulation adminis- trators with the point of getting a veracity score related with each review. Based on this score, it is conceivable to identify spam reviews [20]. Specifically, they provide a definition of aggregation operator F:
Table 1 Description of features in CATS Multicriteria Decision: Viviani et al. [20] used a multicrite- ria choice making approach based both on the evaluation of numerous criteria and the utilize of accumulation adminis- trators with the point of getting a veracity score related with each review. Based on this score, it is conceivable to identify spam reviews [20]. Specifically, they provide a definition of aggregation operator F:
Fig. 2 Wang et al., model overview. They take the products items as the head part of the TransE network in their model, take the reviewers as the translation (relation) part and take the review as the tail part
Fig. 2 Wang et al., model overview. They take the products items as the head part of the TransE network in their model, take the reviewers as the translation (relation) part and take the review as the tail part
Fig. 3 Li et al., model overview. The embedding network consists four parts: user embedding layers, item embedding layers, review embedding networks, and rating embedding layers
Fig. 3 Li et al., model overview. The embedding network consists four parts: user embedding layers, item embedding layers, review embedding networks, and rating embedding layers
Table 2 Mukherjee et al. indicators
Table 2 Mukherjee et al. indicators
Fig.4 Xie et al., model overview. Relationship of Spammers. Reviewers and Customers
Fig.4 Xie et al., model overview. Relationship of Spammers. Reviewers and Customers
Fig.5 Yang et al., WMUSVM Algorithm Overview. This demon- strate builds up hypersphere with greatest volume, containing all beguiling reviews information, and all genuine reviews information are exterior of this hypersphere
Fig.5 Yang et al., WMUSVM Algorithm Overview. This demon- strate builds up hypersphere with greatest volume, containing all beguiling reviews information, and all genuine reviews information are exterior of this hypersphere
Fig.6 Nayak et al., review indicator framework. They demonstrate the review spamicity expectation as a two-class classification issue. The review spamicity pointer was built utilizing stacked LSTM mod- els and show yields the individual probabilities of each review being genuine or fakes
Fig.6 Nayak et al., review indicator framework. They demonstrate the review spamicity expectation as a two-class classification issue. The review spamicity pointer was built utilizing stacked LSTM mod- els and show yields the individual probabilities of each review being genuine or fakes
Table 4 Op_spam_v1.4 Datasets statistics
Table 4 Op_spam_v1.4 Datasets statistics
Table 6 Review datasets in Yelp.com
Table 6 Review datasets in Yelp.com
Table 7 Statistics of the 500 restaurants in Shanghai
Table 7 Statistics of the 500 restaurants in Shanghai
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