<![CDATA[The growth of online reviews on digital platforms has made consumer opinions an important source for understanding perceptions of service quality in businesses. This study aims to analyze aspect-based sentiment and emotion from consumer reviews using the Distilled Bidirectional Encoder Representation from Transformers (DistilBERT) method. Data were collected from Google Reviews and processed through text preprocessing, aspect extraction, sentiment and emotion labeling, and fine-tuning of the DistilBERT model. Sentiment analysis was classified into three classes (positive, negative, and neutral), while emotion analysis included five categories (happy, angry, disappointed, sad, and neutral). The evaluation results show that the DistilBERT model achieved excellent performance in sentiment classification with an accuracy of 95.00%, precision of 93.60%, recall of 95.00%, and F1-score of 94.22%. For emotion classification, the model achieved an accuracy of 94.00%, precision of 88.36%, recall of 94.00%, and F1-score of 91.09%. These findings indicate that a Transformer-based approach is effective in understanding the contextual meaning of consumer reviews despite the use of a relatively limited dataset. This study concludes that DistilBERT is capable of providing accurate and efficient aspect-based sentiment and emotion analysis, which can be utilized as a foundation for evaluating and improving service quality and digital business reputation.]]>
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