Support for Cross-Domain Methods of Identifying Fake Comments of Chinese
[Objective]This paper constructs a cross-domain Chinese fake review identification model(CFEE)for multi-domain datasets.It extracts the semantic information of the comment texts and addresses the problems of traditional recognition models.[Methods]First,we established 11 rules for constructing fake review datasets and created a multi-domain dataset.Then,we designed the CFEE model to identify Chinese fake comments across domains.Third,it extracted the deep semantic information with the ERNIE pre-training model.The model identified the hidden comments based on the texts'emotional attributes.Finally,it projected the text information to the word relation dimension with the convolutional neural network and realized classification based on features of neural network fusion.[Results]The CFEE model's F,value reached 91.52％on the multi-domain Chinese fake comment datasets.The model's F,values were 85.71％,79.59％,85.71％,and 85.00％on single-domain datasets for mobile phones,food,clothing,and household appliances,respectively.It outperformed the existing models significantly.[Limitations]There is subjectivity in the manual annotation.[Conclusions]The proposed method can effectively identify Chinese fake reviews across domains.
NETL
NSTL
万方数据
