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Machine learning-based fake news detection with amalgamated feature extraction method

Author(s)
Muhammad Bux Alvi Department of Computer Systems Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, Pakistan
Majdah Alvi Department of Computer Systems Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, Pakistan
Rehan Ali Shah Department of Computer Systems Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, Pakistan
Adnan Akhter Department of Computer Systems Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, Pakistan
Mubashira Munir Department of Computer Systems Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, Pakistan
Rakesh Kumar Sindh Agriculture University Tandojam, Sindh, Pakistan
Kavita Tabbassum Department Information Technology Center, Sindh Agriculture University Tandojam,Sindh,Pakistan
Abstract
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 non-organic reviewer, so are their intruding approaches. In this paper, different features are amalgamated along with sentiment scores 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, using LIWC, unigrams, and sentiment score features. Results show that the amalgamation of selected features is a better approach to counterfeit review detection, achieving an accuracy score of 88.76%, which is promising when compared to similar other work.
Publication Details
Page(s) 10-17
DOI DOI not available
Published Journal: Sukkur IBA Journal of Emerging Technologies (SJET), Volume: 5, Issue: 2, Year: 2022

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Keywords
Machine learning, sentiment score, LIWC, fake reviews, amalgamated features
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