Sentiment patterns and reported barriers in digital tax administration through an analysis of Coretax Mobile Google Play reviews

Abstract

Indonesia’s transition toward digital tax administration has increased the importance of understanding how users experience public digital services. This study examines sentiment patterns and reported barriers in 820 Google Play reviews of the Coretax Mobile application posted during the January–April 2026 tax-filing period. Reviews were processed using a deterministic text-preprocessing procedure and analyzed through rating-derived sentiment classification, machine-learning models, and Latent Dirichlet Allocation topic modeling. Negative reviews dominated the dataset (93.9%), with a mean rating of 1.25 out of 5. Among the four classifiers, the Support Vector Machine achieved the highest macro-F1 score (0.4377), although its accuracy (0.9463) was only slightly higher than the majority-class baseline (0.9390), indicating limited performance under severe class imbalance. Topic modeling identified three recurring complaint themes: application malfunctions and recurrent failures (31.3%), account access and registration difficulties (36.2%), and tax-reporting and payment complexity (32.5%). These patterns suggest that user difficulties are concentrated not in a single stage of service delivery but across access, system reliability, and task completion. The findings support prioritizing user-centered redesign, technical reliability, and accessible user assistance, while remaining limited to self-selected Google Play reviewers rather than the broader population of Coretax users.