Hybrid transformer-based deep learning model analysis for Review classification in E-commerce
Keywords:
Sentiment Classification, E-commerce Reviews, Deep Learning Models, Transformer-Based ArchitecturesAbstract
As e-commerce platforms continue to grow, understanding customer sentiment has become essential for improving user experiences, refining product recommendations, and supporting decision-making processes. Sentiment classification models play a vital role in analyzing extensive customer reviews. However, accurately categorizing sentiments, particularly neutral ones, remains a significant challenge due to the overlapping nature of textual expressions. This proposed work evaluates the performance of five deep learning models, LSTM, GRU, Bi-LSTM, Bi-GRU, and a hybrid model called ResBERT with Bi-GRU for sentiment classification tasks. A major contribution of this research is the investigation of a Hybrid transformer-based deep learning model designed to improve classification accuracy, particularly in handling delicate or complex sentiments. Experimental results demonstrate that the Hybrid transformer-based deep learning model consistently outperforms the others, achieving the highest accuracy. These findings underscore the potential of the proposed model in advancing sentiment analysis, offering valuable insights for e-commerce platforms seeking to better interpret customer feedback and refine their business strategies
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