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Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain
Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699771/ https://www.ncbi.nlm.nih.gov/pubmed/36438685 http://dx.doi.org/10.1155/2022/4656846 |
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author | Alsubari, Saleh Nagi Aldhyani, Theyazn H. H. Deshmukh, Sachin N. Maashi, Mashael Alharbi, Sadeen Al-Baity, Heyam H. |
author_facet | Alsubari, Saleh Nagi Aldhyani, Theyazn H. H. Deshmukh, Sachin N. Maashi, Mashael Alharbi, Sadeen Al-Baity, Heyam H. |
author_sort | Alsubari, Saleh Nagi |
collection | PubMed |
description | Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model's performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods. |
format | Online Article Text |
id | pubmed-9699771 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-96997712022-11-26 Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain Alsubari, Saleh Nagi Aldhyani, Theyazn H. H. Deshmukh, Sachin N. Maashi, Mashael Alharbi, Sadeen Al-Baity, Heyam H. Comput Intell Neurosci Research Article Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model's performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods. Hindawi 2022-11-18 /pmc/articles/PMC9699771/ /pubmed/36438685 http://dx.doi.org/10.1155/2022/4656846 Text en Copyright © 2022 Saleh Nagi Alsubari et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Alsubari, Saleh Nagi Aldhyani, Theyazn H. H. Deshmukh, Sachin N. Maashi, Mashael Alharbi, Sadeen Al-Baity, Heyam H. Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain |
title | Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain |
title_full | Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain |
title_fullStr | Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain |
title_full_unstemmed | Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain |
title_short | Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain |
title_sort | computational intelligence based recurrent neural network for identification deceptive review in the e-commerce domain |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699771/ https://www.ncbi.nlm.nih.gov/pubmed/36438685 http://dx.doi.org/10.1155/2022/4656846 |
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