Morad

Emotion recognition plays an important role in enhancing social communications and is a key component of improving human-machine interactions. With the rise of deep learning, emotion recognition systems are becoming more effective and accurate. However, progress is often limited by the availability of datasets, a problem affecting low-resourced languages such as the Moroccan Arabic Dialect (Darija). This article introduces the Moroccan Arabic Multimodal Authentic Emotional Dataset (MORAD), a multimodal dataset collected to address the scarcity of affective datasets in the Moroccan Arabic Dialect. MORAD comprises 2325 videos, totaling 3 h, and includes 329 unique speakers expressing their emotions naturally in unscripted real-world settings. The dataset contains seven emotions: 1) happiness; 2) sadness; 3) anger; 4) fear; 5) disgust; 6) surprise; and 7) neutrality. We evaluated various deep learning models on this dataset, including unimodal speech, visual, and textual models, as well as a multimodal approach. The multimodal model achieved the highest performance with a weighted F1 score of 73.11% and an accuracy of 73.36% on a 4-way classification task, and 66.69% and 69.38%, respectively, on the 7-way task. These results provide a benchmark demonstrating MORAD’s utility for emotion recognition

Arabic · Visualization · Emotion and Mood Recognition · Hate Speech and Cyberbullying Detection · Sentiment Analysis and Opinion Mining