This study addresses multi-label emotion classification in Moroccan Arabic. We developeda lightweight computational approach to detect and categorize emotional content in sevendistinct categories: anger, fear, joy, disgust,sadness, surprise, and neutral. Our findings reveal that our efficient, subword-aware modelachieves 46.44% accuracy on the task, demonstrating the viability of lightweight approachesfor emotion recognition in under-resourcedlanguage variants. The model’s performance,while modest, establishes a baseline for emotion detection in Moroccan Arabic, highlighting both the potential and challenges of applying computationally efficient architectures to dialectal Arabic processing. Our analysis revealsparticular strengths in handling morphologicalvariations and out-of-vocabulary words, thoughchallenges persist in managing code-switchingand subtle emotional distinctions. These results offer valuable insights into the trade-offsbetween speed and accuracy in multilingualemotion detection systems, particularly for low-resource languages.