Aspect-Based Sentiment Analysis on Skintific Product Reviews Using IndoBERT - Asyifa Hafizah Putri; Christian Sri Kusuma Aditya (Forum Kerjasama Pendidikan Tinggi, 2026)
The rapid growth of the beauty industry has generated a massive volume of online reviews where traditional sentiment analysis fails to capture contradictory opinions across specific product features. This study implements Aspect-Based Sentiment Analysis (ABSA) using the IndoBERT-base-p1 architecture on 2,139 review data points from Female Daily, integrated with a specialized slang normalization stage to mitigate linguistic noise. The novelty lies in evaluating IndoBERT’s bidirectional attention robustness in processing technical medical terminology alongside Indonesian social media slang—a complexity often overlooked in prior beauty domain studies. This study contributes a novel methodological pipeline that bridges deep learning architectures with domain-specific linguistic preprocessing, providing a benchmark dataset for Indonesian beauty product reviews. The results showed that IndoBERT was able to distinguish nuances of sentiment, with superior performance in the Effectiveness (F1-Score 72.57%) and Texture (F1-Score 71.10%). Although the average score was affected by sample limitations in certain aspects, the model proved effective in capturing the semantics of Indonesian consumer slang. Ultimately, this research provides a practical contribution for consumers in validating product quality specifically and for producers as a basis for evaluating product performance in the public eye.