###### Abstract  
Emotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emotion recognition with limited labeled data becomes prominent. Moreover, emotion recognition for some languages, such as Arabic, is a challenging task due to the limited availability of labeled data. This scarcity exists in both size and the granularity of emotions. Our research introduces a novel framework for low-resource fine-grained emotion recognition, which uses an iterative process that integrates a stacking ensemble of diverse base models and self-training. The base models employ different learning paradigms, including zero-shot classification, few-shot methods, machine learning algorithms, and transfer learning. Our proposed method eliminates the need for a large labeled dataset to initiate the training process by gradually generating labeled data through iterations. During our experiments, we evaluated the performance of each base model and our proposed method in low-resource scenarios. Our experimental findings indicate our approach outperforms the individual performance of each base model. It also outperforms the state-of-the-art Arabic emotion recognition models in the literature, achieving a weighted average F1-score equal to 83.19% and 72.12% when tested on the AETD and ArPanEmo benchmark datasets, respectively. [table]capposition=top
domains. However, with the advancement of ML techniques, particularly deep learning models, emotion recognition has evolved [7; 8; 9; 10; 11]. Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs) [12], Recurrent Neural Networks (RNNs) [13], and more recently Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT) [14] and Generative Pretrained Transformer (GPT) [15], have shown remarkable performance in various text classification tasks, including emotion recognition [16; 17; 18; 19; 20]. These models can automatically learn features and representations from raw texts, avoiding the need for hand-engineered features. Training a model for fine-grained emotion recognition requires a substantial amount of labeled data. However, the data can be limited for specialized domains or some granular emotion categories. Moreover, fine-grained classification often entails a large number of classes, making it challenging to maintain balanced datasets across classes [21]. The challenges faced by emotion recognition models, such as handling granular emotions, the requirement for a substantial amount of labeled data, and performing cross-domain emotion recognition, are applicable to many languages, including Arabic and English. However, when comparing Arabic to English, research on Arabic emotion recognition lacks annotated datasets. A recent survey on Arabic emotion recognition [22] revealed the limited availability of labeled datasets and emotion lexicons for Arabic. This challenge is intensified by the diversity of Arabic varieties in use today, including Modern Standard Arabic (MSA) and various Arabic dialects, which further complicates the task of creating labeled data for different Arabic varieties. In this paper, we propose a novel method that addresses the challenges of low-resource fine-grained Arabic emotion recognition. Central to our methodology is an iterative framework that integrates a stacking ensemble of diverse models and self-training. The ensemble model, also known as the meta-learner or stacker, takes the predictions generated by the base models as input features and learns to combine them to make the final prediction. The base models include machine learning models, few-shot models, BERT-based model, and a zero-shot classification model. On the other hand, the base models and self-training with majority voting are utilized to carefully and gradually increase the amount of labeled data in each iteration.