From Machine Learning to Heterogeneous Models: A Comprehensive Study on Fine-Grained Emotion Detection in Arabic Text - Alshdaifat, Esra’a; Altarawneh, Enshirah; Aloshaibat, Mogeeb Alrahman; Hussein, Anas; Dawood, Mohammed; Tahaineh, Tawfiq (Engineering, Technology & Applied Science Research, 2026)
Recognizing emotions from Arabic text has received increasing attention in the last few years, due to its significant role in several real-world applications. This study begins by employing several classic Machine Learning (ML), Deep Learning (DL), and pre-trained Large Language Models (LLMs) to construct emotion detection models for Arabic text. Then, a composite model is proposed that integrates the most-effective model from each considered category. More precisely, two main strategies are examined to combine the most-effective models: (i) a parallel strategy and (ii) a sequential strategy. Adopting the parallel strategy, the base models are independent and combined utilizing majority voting or average probability techniques, whereas in the sequential strategy the models are dependent and different weights are assigned to the base models. According to the extensive experiments conducted, the best F1-scores obtained from ML, DL, and pre-trained LLMs were 0.668, 0.742, and 0.794, respectively. On the other hand, the sequential composite model produced an F1-score of 0.802, outperforming the parallel models, which reached an F1-score of 0.790. The findings highlight the power of sequential hybrid models and confirm that integrating different architectural models in a way that one model passes information to the succeeding model, focusing on learning hard instances, outperforms all considered models.