Abstract
The user’s commodity reviews displayed on ecommerce platforms will affect consumers’ consumption decisions. The detection of fake reviews can provide reference for consumers to screen the review information and protect the legitimate rights and interests of consumers and merchants. In order to improve the detection effect of fake Chinese reviews, this paper proposes a fake review detection model based on ERNIE-BiLSTM, which takes fake review detection as a text classification task, uses ERNIE network layer to generate dynamic word vectors, and uses BiLSTM neural network to extract features. Through the test experiments on the data set of fake reviews in Chinese commodities collected and labeled, the ERNIE-BiLSTM model was compared with the BERT-BiLSTM model in terms of accuracy, recall rate, precision and F1 score. The results show that the ERNIE-BiLSTM model is superior to other models in the detection of fake reviews.
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