A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

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.

Automated summary

Machine learning frameworks have improved sales and product quality for major manufacturers by categorizing customer reviews on online products. Manual scrutiny of extensive customer reviews is imprecise and time-consuming. Current research techniques neglect audio and image components, resulting in less productive outcomes. AI-based frameworks that consider social media and online buyer reviews are essential for accurate recommendations.