Analisis Sentimen Berbasis Aspek Ulasan Produk Menggunakan CNN dan Bidirectional LSTM

Aspect-Based Sentiment Analysis Product Review Using CNN and Bidirectional LSTM

Abstract

 Abstract — The COVID-19 pandemic has transformed consumer lifestyles in Indonesia, notably increasing the use of e-commerce platforms due to social restrictions. This shift has influenced how consumers evaluate product quality, making consumer reviews a crucial element in purchasing decisions. Traditional sentiment analysis falls short in providing detailed insights into product aspects, making Aspect-Based Sentiment Analysis (ABSA) a promising solution. Deep learning models like Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) offer high accuracy in sentiment analysis. This study analyzes consumer sentiment towards e-commerce product aspects in Indonesia by applying ABSA, addressing the challenges of implementation in the Indonesian language, and measuring the accuracy and effectiveness of the hybrid CNN Bi-LSTM model. The methodology includes dataset preprocessing, aspect extraction and sentiment classification, data training and prediction, and model evaluation. The results show that the CNN Bi-LSTM model achieves an average accuracy of 90% for aspect extraction and 92% for sentiment classification. In conclusion, despite dataset limitations, optimal data preparation and the hybrid model effectively facilitate ABSA.

Key Word — ABSA; CNN; Bi-LSTM; Accuracy; Hybrid Model

Abstrak — Pandemi COVID-19 telah mengubah gaya hidup konsumen di Indonesia, terutama melalui peningkatan penggunaan platform e-commerce akibat pembatasan sosial. Perubahan ini mempengaruhi cara konsumen menilai kualitas produk, menjadikan ulasan konsumen elemen kunci dalam keputusan pembelian. Analisis sentimen tradisional tidak memadai untuk memberikan pemahaman mendetail tentang aspek produk, sehingga Aspect-Based Sentiment Analysis (ABSA) menjadi solusi yang menjanjikan. Model deep learning seperti Convolutional Neural Network (CNN) dan Bidirectional Long Short-Term Memory (Bi-LSTM) menawarkan akurasi tinggi dalam analisis sentimen. Penelitian ini menganalisis sentimen konsumen terhadap aspek produk e-commerce di Indonesia dengan menerapkan ABSA, menghadapi tantangan implementasi dalam bahasa Indonesia, dan mengukur akurasi serta efektivitas model hybrid CNN Bi-LSTM. Metodologi meliputi preprocessing dataset, aspect extraction dan sentiment classification, data training dan prediction, serta evaluasi model. Hasil menunjukkan model CNN Bi-LSTM memiliki akurasi rata-rata 90% untuk aspect extraction dan 92% untuk sentiment classification. Kesimpulannya, data preparation optimal meskipun ada keterbatasan dataset, dan model hybrid efektif untuk ABSA.

Kata kunci — ABSA; CNN; Bi-LSTM; Akurasi; Hybrid Model