Fake online review recognition algorithm and optimisation research based on deep learning
- Autores: Jiang-Liang Hou, Aimin Zhu
- Localización: Applied Mathematics and Nonlinear Sciences, ISSN-e 2444-8656, Vol. 7, Nº. 2, 2022, págs. 861-874
- Idioma: inglés
- Enlaces
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Resumen
With the rapid development of the e-commerce industry, online reviews of goods are a great help for consumers to makedecisions. With the sharp increase in online order for goods and the explosion of product reviews, some merchants beganto hire consumers to make fake purchases for profit, which led to the problem of identifying fake reviews. In this paper, wepropose a method that uses feature engineering to eliminate the comments of false reviewers and combines convolutionalneural network and recurrent neural network to classify and recognise reviews from the perspective of text. Traditionalneural network models such as CNN, LSTM and BILSTM are compared with the hybrid model proposed by the text. Themodel is optimised by pre-training on the Baidu Baike commodity review database instead of the initial randomising wordvector. The experimental results show that the combination of convolutional neural network and recurrent neural networkcan better extract the global and local features of false comments, and the model has a good effect. The updating of thepre-trained word vector makes the recognition effect of each model better
