Multi-Task Mining of Ethiopian Mobile App Reviews Using Machine Learning and Deep Learning Approaches - Tegegnie, Alemu Kumilachew (International Journal of Interactive Mobile Technologies (iJIM), 2026)
The rapid growth of mobile applications in Ethiopia has generated a wealth of user-generated content in the form of app reviews and ratings. These reviews provide critical insights into user satisfaction, app performance, and feature demands. However, systematic analysis of such unstructured and multilingual feedback remains limited in Ethiopia due to the absence of automated tools and localized natural language processing (NLP) resources. This study introduces a multi-task review mining framework that integrates sentiment classification, feedback categorization, and rating prediction. A dataset of 10,200 Ethiopian mobile app reviews collected from the Google Play Store was preprocessed, annotated, and analyzed using both machine learning and deep learning models. Experimental results indicate that convolutional neural networks (CNNs) outperformed other models, achieving 98.7% accuracy for sentiment classification, 96.6% for feedback categorization, and an R2 of 0.40 for rating prediction. Among traditional models, XGBoost demonstrated strong performance, particularly in classification tasks. The findings highlight the effectiveness of CNN-based models in extracting actionable insights from multilingual reviews, offering developers and policymakers data-driven tools to improve app quality and enhance user satisfaction. This study contributes to the growing field of opinion mining in low-resource contexts and aligns with Ethiopia’s Digital Transformation 2025 agenda.