A Hybrid Deep Learning Approach for Enhanced Sentiment Classification and Consistency Analysis in Customer Reviews - Shaymaa E. Sorour; Abdulrahman Alojail; Amr El-Shora; Ahmed E. Amin; Amr A. Abohany (Mathematics)
Consumer reviews play a pivotal role in shaping purchasing decisions and influencing the reputation of businesses in today’s digital economy. This paper presents a novel hybrid deep learning model, WDE-CNN-LSTM, designed to enhance the sentiment classification of consumer reviews. The model leverages the strengths of Word Embeddings (WDE), Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs) to capture temporal and local text data features. Extensive experiments were conducted across binary, three-class, and five-class classification tasks, with the proposed model achieving an accuracy of 98% for binary classification, 98% for three-class classification, and 95.21% for five-class classifications. The WDE-CNN-LSTM model consistently outperformed standalone CNN, LSTM, and WDE-LSTM models regarding precision, recall, and <semantics>F1</semantics>-score, achieving up to 98.26% in <semantics>F1</semantics>-score for three-class classification. The consistency analysis also revealed a high alignment between the predicted sentiment and customer ratings, with a consistency rate of 96.00%. These results demonstrate the efficacy of this hybrid architecture in handling complex sentiment classification tasks (SCTs), offering significant improvements in accuracy, classification metrics, and sentiment consistency. The findings have important implications for improving sentiment analysis in customer review systems, contributing to more reliable and accurate sentiment classification.