EduDarijaBERT: An Applied Transformer-Based Framework for Sentiment Analysis in Moroccan Arabic Educational Discourse
TL;DR
This study provides universities with a stable, easily implementable system to track student feedback and fuel data-driven innovations, which utilizes EduDarijaBERT—a transformer model explicitly fine-tuned on this academic domain.
Abstract
Students' opinions play a pivotal role in the formulation of successful educational policies. However, the computational analysis of the Moroccan Arabic Dialect remains widely viewed as problematic due to a focus on complex morphology and the absence of specific datasets. To close this gap, we propose an end-to-end system for educational sentiment mining, which utilizes EduDarijaBERT—a transformer model explicitly fine-tuned on this academic domain. In the development of the system, we initially constructed and systematically assessed a specifically curated corpus of 12,223 social media comments. To assess the model's reliability, we evaluated the framework on an independent test set. The proposed EduDarijaBERT system achieved an accuracy of 83.15% on this dataset, demonstrating strong generalization capabilities for educational sentiment mining. In addition to these quantitative metrics, the system facilitates a qualitative interpretation of sentiment categories, providing practical insights revealing that administrative challenges are among the major contributors to negative feedback, while active academic support is the key to positive interaction. This study provides universities with a stable, easily implementable system to track student feedback and fuel data-driven innovations.