📅 01 April 2025
DOI: 10.62411/jcta.12376
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
🔖 Keywords
#Aspect-based sentiment analysis; Attention mechanism; BiDAF; E-commerce reviews analysis; Emoji handling; Lemmatization; Slang normalization
ℹ️ Informasi Publikasi
Tanggal Publikasi
01 April 2025
Volume / Nomor / Tahun
Volume 2, Nomor 4, Tahun 2025
📝 HOW TO CITE
Setiadi, De Rosal Ignatius Moses; Warto, Warto; Muslikh, Ahmad Rofiqul; Nugroho, Kristiawan; Safriandono, Achmad Nuruddin, "Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow," Journal of Computing Theories and Applications, vol. 2, no. 4, Apr. 2025.
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