Analisis Sentimen Berbasis Aspek Ulasan GoFood di X dengan Ekstraksi Aspek Rule-Based dan Klasifikasi IndoBERT

Social media reviews provide insights into user perceptions of food delivery services, but general sentiment analysis does not identify specific service aspects. This study applies Aspect-Based Sentiment Analysis (ABSA) using a hybrid Rule-Based and IndoBERT approach to GoFood reviews on X. Rule-Based methods extract aspects using keywords and structured rules, while IndoBERT classifies sentiment based on textual context. From 28,763 Indonesian-language tweets obtained from Kaggle, 5,657 aspect–sentiment pairs were obtained after preprocessing, aspect extraction, auditing, and manual verification. The dataset covers four aspects: price, service, food quality, and application. Using a StratifiedGroupKFold split of 70:15:15, IndoBERT achieved 90.31% accuracy and a Macro F1-score of 0.8883 on 846 test samples. Food quality achieved the highest aspect-specific accuracy (92.62%), followed by price (90.40%), service (90.00%), and application (87.88%). Price was the most frequently identified aspect, while positive sentiment dominated food quality (60.4%) and neutral sentiment dominated the other three aspects. These findings provide detailed aspect-level insights into user perceptions and can help identify service aspects to maintain or examine further. However, the impact on service quality improvement has not been directly evaluated.