<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid development of the e-commerce industry, online reviews of goods are a great help for consumers to make decisions. With the sharp increase in online order for goods and the explosion of product reviews, some merchants began to hire consumers to make fake purchases for profit, which led to the problem of identifying fake reviews. In this paper, we propose a method that uses feature engineering to eliminate the comments of false reviewers and combines convolutional neural network and recurrent neural network to classify and recognise reviews from the perspective of text. Traditional neural network models such as CNN, LSTM and BILSTM are compared with the hybrid model proposed by the text. The model is optimised by pre-training on the Baidu Baike commodity review database instead of the initial randomising word vector. The experimental results show that the combination of convolutional neural network and recurrent neural network can better extract the global and local features of false comments, and the model has a good effect. The updating of the pre-trained word vector makes the recognition effect of each model better.</p>
</abstract>

With the rapid development of the e-commerce industry, online reviews of goods are a great help for consumers to make decisions. With the sharp increase in online order for goods and the explosion of product reviews, some merchants began to hire consumers to make fake purchases for profit, which led to the problem of identifying fake reviews. In this paper, we propose a method that uses feature engineering to eliminate the comments of false reviewers and combines convolutional neural network and recurrent neural network to classify and recognise reviews from the perspective of text. Traditional neural network models such as CNN, LSTM and BILSTM are compared with the hybrid model proposed by the text. The model is optimised by pre-training on the Baidu Baike commodity review database instead of the initial randomising word vector. The experimental results show that the combination of convolutional neural network and recurrent neural network can better extract the global and local features of false comments, and the model has a good effect. The updating of the pre-trained word vector makes the recognition effect of each model better.

Fake online review recognition algorithm and optimisation research based on deep learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the rapid development of the e-commerce industry, online reviews of goods are a great help for consumers to make decisions. With the sharp increase in...

Fake online review recognition algorithm and optimisation research based on deep learning

Data publikacji: 31 mar 2022

Zakres stron: 861 - 874

Otrzymano: 20 lip 2021

Przyjęty: 06 gru 2021

DOI: https://doi.org/10.2478/amns.2021.2.00170

Słowa kluczowe
fake review identification, deep learning, the neural network

© 2021 Jiani Hou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fake online review recognition algorithm and optimisation research based on deep learning

Data publikacji: 31 mar 2022

Zakres stron: 861 - 874

Otrzymano: 20 lip 2021

Przyjęty: 06 gru 2021

DOI: https://doi.org/10.2478/amns.2021.2.00170

Słowa kluczowefake review identification, deep learning, the neural network

© 2021 Jiani Hou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
fake review identification, deep learning, the neural network