Affiliation:
1. Department of Information Systems, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University Jeddah, Jeddah 34025, Saudi Arabia
Abstract
Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.
Funder
institutional Fund Projects
Ministry of Education and King Abdul Aziz University, DSR, Jeddah, Saudi Arabia
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
2 articles.
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