Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 4
July-August 2026

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An Intelligent Bayesian Deep Learning Framework for Customer Opinion Mining Using Amazon Reviews

Author(s) Mr. Abdul Hamid
Country India
Abstract The rapid expansion of electronic commerce platforms has generated enormous amounts of customer-generated textual feedback, making automated opinion mining an essential component of intelligent decision support systems. Extracting meaningful knowledge from online reviews enables organizations to understand customer preferences, monitor product quality, and improve marketing strategies. Although numerous machine learning and deep learning algorithms have been proposed for sentiment analysis, many existing approaches suffer from overfitting, high computational complexity, and poor performance on imbalanced datasets. This paper proposes an intelligent Bayesian deep learning framework for customer opinion mining using Amazon product reviews. The framework integrates comprehensive text preprocessing, TF-IDF-based feature representation, Bayesian probabilistic learning, and neural network optimization through Bayesian regularization. The proposed approach effectively models nonlinear sentiment relationships while improving generalization capability. Experimental evaluation performed on the Amazon Customer Review dataset demonstrates superior performance compared with conventional machine learning and deep learning techniques. The proposed framework achieved 99.34% classification accuracy, 99.18% precision, 99.12% recall, and 99.15% F1-score while converging within only 45 training epochs. Comparative analysis confirms that Bayesian learning significantly improves robustness, scalability, and prediction reliability, making the proposed framework suitable for intelligent e-commerce analytics and large-scale customer opinion mining applications.
Keywords Opinion Mining, Bayesian Deep Learning, Customer Reviews, Sentiment Analysis, Artificial Neural Network, Natural Language Processing, E-Commerce.
Field Engineering
Published In Volume 7, Issue 4, July-August 2026
Published On 2026-07-11
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E-ISSN 3048-7641
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