Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
Online reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3771 |
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| author | Prathana Phukon Petros Potikas Katerina Potika |
| author_facet | Prathana Phukon Petros Potikas Katerina Potika |
| author_sort | Prathana Phukon |
| collection | DOAJ |
| description | Online reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated (genuine) as well as computer-generated (fake) reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this work, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem. |
| format | Article |
| id | doaj-art-1df00d1937dc480c8a6aa21a93a65451 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1df00d1937dc480c8a6aa21a93a654512025-08-20T03:06:32ZengMDPI AGApplied Sciences2076-34172025-03-01157377110.3390/app15073771Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional NetworksPrathana Phukon0Petros Potikas1Katerina Potika2Computer Science Department, San Jose State University, San Jose, CA 95192, USAElectrical and Computer Engineering School, National Technical University of Athens, 15780 Athens, GreeceComputer Science Department, San Jose State University, San Jose, CA 95192, USAOnline reviews significantly influence consumer behavior and business reputations. Detecting fake reviews is important for maintaining trust and integrity in these platforms. We present an aspect-based sentiment analysis approach, referred to as FakeDetectionGCN, to distinguish genuine feedback from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph convolutional networks are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated (genuine) as well as computer-generated (fake) reviews. It has been evaluated on two types of datasets and has shown strong performance across both. Through this work, we contribute to the effective detection of fake reviews and maintaining a trustworthy online review ecosystem.https://www.mdpi.com/2076-3417/15/7/3771fake reviewsaspect-based sentiment analysisgraph neural networks |
| spellingShingle | Prathana Phukon Petros Potikas Katerina Potika Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks Applied Sciences fake reviews aspect-based sentiment analysis graph neural networks |
| title | Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks |
| title_full | Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks |
| title_fullStr | Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks |
| title_full_unstemmed | Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks |
| title_short | Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks |
| title_sort | detecting fake reviews using aspect based sentiment analysis and graph convolutional networks |
| topic | fake reviews aspect-based sentiment analysis graph neural networks |
| url | https://www.mdpi.com/2076-3417/15/7/3771 |
| work_keys_str_mv | AT prathanaphukon detectingfakereviewsusingaspectbasedsentimentanalysisandgraphconvolutionalnetworks AT petrospotikas detectingfakereviewsusingaspectbasedsentimentanalysisandgraphconvolutionalnetworks AT katerinapotika detectingfakereviewsusingaspectbasedsentimentanalysisandgraphconvolutionalnetworks |