Machine learning-based Fake reviews detection with amalgamated features extraction method
Product fake reviews are increasing as the trend is changing toward online sales and purchases. Fake review detection is critical and challenging for both researchers and online retailers. As new techniques are introduced to catch the fake reviewer, so are their intruding approaches. In this paper,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sukkur IBA University
2023-01-01
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| Series: | Sukkur IBA Journal of Emerging Technologies |
| Online Access: | http://journal.iba-suk.edu.pk:8089/sibajournals/index.php/sjet/article/view/1091 |
| Summary: | Product fake reviews are increasing as the trend is changing toward online sales and purchases. Fake review detection is critical and challenging for both researchers and online retailers. As new techniques are introduced to catch the fake reviewer, so are their intruding approaches. In this paper, different features are amalgamated along with sentiment score to design a model that checks the model performance under different classifiers. For this purpose, six supervised learning algorithms are utilized to build the fake review detection models, utilizing LIWC, unigrams, and sentiment score features. Results show that the amalgamation of selected features is a better approach to fake review detection, achieving an accuracy score of 88.76%, which is promising compared to similar other work.
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| ISSN: | 2616-7069 2617-3115 |