Fake online review recognition algorithm and optimisation research based on deep learning
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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-03-01
|
| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2021.2.00170 |
| _version_ | 1797258392333451264 |
|---|---|
| author | Hou Jiani Zhu Aimin |
| author_facet | Hou Jiani Zhu Aimin |
| author_sort | Hou Jiani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:41:37Z |
| format | Article |
| id | doaj.art-9d9ac17962304ae7a91c5cdbde35f788 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-04-24T22:52:48Z |
| publishDate | 2022-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-9d9ac17962304ae7a91c5cdbde35f7882024-03-18T10:29:01ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562022-03-017286187410.2478/amns.2021.2.00170Fake online review recognition algorithm and optimisation research based on deep learningHou Jiani0Zhu Aimin1College of Management, Shenyang University of Technology, Shenyang110870, Liaoning, ChinaCollege of Management, Shenyang University of Technology, Shenyang110870, Liaoning, ChinaWith 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.https://doi.org/10.2478/amns.2021.2.00170fake review identificationdeep learningthe neural network |
| spellingShingle | Hou Jiani Zhu Aimin Fake online review recognition algorithm and optimisation research based on deep learning Applied Mathematics and Nonlinear Sciences fake review identification deep learning the neural network |
| title | Fake online review recognition algorithm and optimisation research based on deep learning |
| title_full | Fake online review recognition algorithm and optimisation research based on deep learning |
| title_fullStr | Fake online review recognition algorithm and optimisation research based on deep learning |
| title_full_unstemmed | Fake online review recognition algorithm and optimisation research based on deep learning |
| title_short | Fake online review recognition algorithm and optimisation research based on deep learning |
| title_sort | fake online review recognition algorithm and optimisation research based on deep learning |
| topic | fake review identification deep learning the neural network |
| url | https://doi.org/10.2478/amns.2021.2.00170 |
| work_keys_str_mv | AT houjiani fakeonlinereviewrecognitionalgorithmandoptimisationresearchbasedondeeplearning AT zhuaimin fakeonlinereviewrecognitionalgorithmandoptimisationresearchbasedondeeplearning |