Amirkabir University of Technology AUT Journal of Modeling and Simulation 2588-2953 58 2 2026 10 01 Labeling Text Reviews for Customer Churn Prediction via Active Learning, Semi-Supervised Learning, and Explainable AI 6152 10.22060/miscj.2026.25707.5487 EN Soroush Bajoghli Faculty of Computer Engineering, University of Isfahan Fakhroddin Noorbehbahani Faculty of Computer Engineering, University of Isfahan Journal Article 2026 04 20 Customer churn prediction is critical for businesses, yet most prior work relies on structured data and makes limited use of textual reviews, leaving low-resource languages such as Persian underexplored. Text-based churn models require large labeled corpora, which are often expensive and time-consuming to produce. To address this, we propose a practical method that combines least-confidence active learning, explainable AI (LIME) to assist expert annotators, and iterative semi-supervised labeling (self-training, label propagation, and a hybrid method). To our best knowledge there is no study on using active learning, semi-supervised learning and explainable AI to label textual data for churn prediction. Beginning with an expert-verified seed of 2,004 balanced Persian reviews, we trained LSTM/GRU classifiers on ParsBERT embeddings, augmented training sets via active sampling (100 samples per fold), and expanded labels to 10,500 Digikala reviews over 12 semi-supervised stages. Active learning increased average accuracy from 82.5% to 84.7%, and self-training produced the best sustained gains (mean peak accuracies 86%), outperforming label propagation and the hybrid approach. Moreover, the explanations generated by LIME were positively evaluated by three domain experts, with mean ratings exceeding 4 for two experts and above 3.9 for the third, reflecting consistently high satisfaction. In addition, expert agreement increased by up to 8%, reaching 0.91 after reviewing the explanations. The proposed framework offers a reproducible solution for churn prediction in low-resource text domains by introducing the first labeled Persian review dataset for churn prediction and a unified framework integrating active learning, semi-supervised learning, and explainable AI.