Journal of Guangxi Normal University(Natural Science Edition) ›› 2023, Vol. 41 ›› Issue (5): 26-36.doi: 10.16088/j.issn.1001-6600.2023020502

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Chinese Fake Review Detection Based on Attention Convolutional Neural Network

WU Zhengqing, CAO Hui*, LIU Baokai   

  1. Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education (Northwest Minzu University), Lanzhou Gansu 730030, China
  • Received:2023-02-05 Revised:2023-04-02 Published:2023-10-09

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Abstract: A convolutional neural network model based on multi-level attention mechanism is proposed to solve the problem that the existing methods of fake review detection do not make full use of the text features of fake reviews. Firstly, a variety of pre-trained word vectors are used to initialize the word embedding layer, and complex position coding is carried out. Then, multiple feature maps are obtained by convolution of multiple convolution kernels through the channel level and convolution kernel level attention layer embedded with user features, and different weights are assigned according to the importance of features. Finally, the feature representation of the fitted reviews text is classified by softmax. Experimental results show that compared with many mainstream excellent neural network models, the accuracy rate of the proposed model increases by 4.74%, and the F1 value gains by 3.86%.

Key words: fake review detection, attention mechanism, convolutional neural network, pre-trained word vector

CLC Number:  TP391.1
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