Multi-aspect sentiment classification on USA-based Amazon customer reviews with DeBERTa-v3 encoders and aspect attention
Aspect-level sentiment analysis of e-commerce reviews is important for identifying fine-grained customer experience signals that are often hidden by overall star ratings. This study proposes a dual-track framework for USA-based Amazon customer reviews, combining text-driven multi-aspect sentiment classification with structured-feature satisfaction prediction. For the text-based task, reviews are modeled across three operational aspects: Product & Value, Delivery & Fulfillment, and Service & Usability, each using a three-class sentiment scheme of Negative, Neutral, and Positive. The proposed deep learning model integrates DeBERTa-v3 contextual embeddings, bidirectional recurrent refinement, and aspect-specific additive attention to separate overlapping sentiment cues within the same review. Three recurrent variants, Bi-GRU, Bi-RNN, and Bi-LSTM, are evaluated under the same modeling framework. On the held-out Amazon Customer Reviews test set, the DeBERTa-v3 + Bi-GRU + aspect-attention model achieves the best overall performance, with 0.737 accuracy, 0.757 macro-F1, and 0.760 weighted-F1, outperforming the Bi-LSTM and Bi-RNN variants. Ablation analysis further shows that sequential refinement and aspect-specific attention improve performance over encoder-only and shared-attention configurations. In the structured-feature satisfaction task, LightGBM achieves the strongest result among the evaluated machine learning models, with 82.37% accuracy and an F1-score of 0.82, outperforming XGBoost and CatBoost. The results show that the proposed framework provides complementary insights: structured models support overall satisfaction prediction, while the DeBERTa-v3-based aspect model provides more detailed attribution of customer experience across product, delivery, and usability dimensions. Overall, the study offers a reproducible and interpretable approach for fine-grained review analytics in e-commerce settings.