Hybrid RoBERTa-BERTopic Sentiment Analysis for Information Governance at PT Bank Central Asia Tbk - Divasaputra, Pastika; Sumirat Hidayat, Deden (Jurnal Tekno Kompak, 2026)
Consumer reviews on digital distribution platforms offer valuable insights for financial institutions to evaluate service quality and user satisfaction. This study implements an integrated natural language processing (NLP) framework combining the pre-trained indonesian-roberta-base-sentiment-classifier model and BERTopic to perform granular sentiment analysis and thematic topic modeling on mobile banking user feedback. A total of 3,138 validated customer reviews were harvested from the Google Play Store. The sentiment classification revealed that 52.1% of the reviews expressed positive sentiments, while 36.0% were categorized as negative and 11.9% were categorized as neutral. Through cross-matrix BERTopic modeling, these sentiments were successfully mapped into critical operational themes, including application technical bugs, post-update system stability, and physical bank teller queue issues. The evaluation of the RoBERTa model demonstrated strong performance with an overall accuracy of 89.5% and a macro F1-score of 88.7%, proving its robustness in processing non-standard Indonesian text. The cross-analysis maps granular actionable insights, enabling banking management to systematically prioritize software engineering enhancements and customer service interventions based on quantified user pain points.