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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Nadya Salsabila Mustafa
Satya Wacana Christian University
Computer Science & IT

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Hybrid Deep Learning Approach Using SiEBERT and LSTM for Sentiment Analysis of E-Commerce Product Reviews Nadya Salsabila Mustafa; Radius Tanone
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The rapid growth of electronic commerce platforms has generated a large volume of user-generated product reviews, increasing the need for accurate sentiment analysis. This study proposes a hybrid deep learning architecture that combines a sentiment-optimized transformer, SiEBERT, as a frozen encoder with a Long Short-Term Memory (LSTM) network as a sequence classifier. The model is evaluated on an Amazon review dataset containing 34,626 instances labeled into positive and negative sentiments. Three configurations are compared: SiEBERT as a direct classifier, LSTM trained from scratch, and the proposed hybrid model. Experimental results show that SiEBERT achieves 73.72% accuracy, while LSTM reaches 57.00%. The hybrid model achieves the best performance with 93.76% accuracy and a weighted F1 score of 94.15%. These findings indicate that combining contextual embeddings with sequential modeling produces more effective sentiment representations. This study contributes by introducing a hybrid SiEBERT–LSTM architecture that addresses limitations of standalone models and significantly improves classification performance, particularly in handling imbalanced sentiment data
Co-Authors Radius Tanone