This paper introduces a novel deep learning (DL) model designed to detect and analyze inconsistencies between customer reviews and ratings on e-commerce platforms. The model aims to classify customer reviews as positive or negative based on their polarity, and then assess the congruency between these classifications and the actual customer ratings. The proposed model is evaluated on a large dataset of Amazon product reviews, demonstrating superior performance in prediction accuracy and other performance measures compared to existing models. The paper also discusses the motivation behind the model, its contributions, and the experimental setup. The results show that the proposed model outperforms other methods in terms of accuracy, precision, recall, and F1-score, making it a valuable tool for enhancing the reliability of review systems and aiding customers in making informed purchasing decisions.This paper introduces a novel deep learning (DL) model designed to detect and analyze inconsistencies between customer reviews and ratings on e-commerce platforms. The model aims to classify customer reviews as positive or negative based on their polarity, and then assess the congruency between these classifications and the actual customer ratings. The proposed model is evaluated on a large dataset of Amazon product reviews, demonstrating superior performance in prediction accuracy and other performance measures compared to existing models. The paper also discusses the motivation behind the model, its contributions, and the experimental setup. The results show that the proposed model outperforms other methods in terms of accuracy, precision, recall, and F1-score, making it a valuable tool for enhancing the reliability of review systems and aiding customers in making informed purchasing decisions.