## 3 Methodology  
Our approach, which aimed at enhancing the performance of low-resource, fine-grained text classification, primarily entails two components: a stacking ensemble of diverse models and self-training, as illustrated in the general framework shown in Figure 1.  
The proposed approach does not require a considerable amount of labeled training data. A small quantity of labeled data for each emotion category proves sufficient to initiate the entire process. As the two components of the proposed method undergo iterations, the performance of the trained classifier (i.e., the stacking ensemble model) improves, along with an increase in the volume of labeled data automatically generated throughout the entire process.  
The first component is a meta-ensemble technique that uses a two-level architecture to conduct emotion recognition. The first level aims to create a set of diverse models using different learning paradigms, including conventional machine learning models, few-shot models, zero-shot classification models, and transfer learning models. This diversity can result in more accurate and comprehensive predictions; that is, each type of learning model may excel in different types of data and tasks [64]. The second level involves constructing a higher-level model (meta-learner or stacker) that learns how to effectively  
combine the predictions of the base models to improve the overall performance on Arabic emotion recognition.  
The second component of our proposed framework is responsible for the iterative aspect of the method. Self-training involves using the base classifiers built in the first component to iteratively label unlabeled data and then incorporating the confident predictions into the training dataset. To determine the most confident predictions, the algorithm applies majority voting to select the predictions with the highest number of votes. As iterations continue, the base classifiers collaborate to improve their performance, and consequently, the meta-learner also continues to improve. The stopping criterion can be set as either a specific number of iterations or when performance plateaus, indicating that the meta-learner's performance has reached a plateau on a validation or development set.  
More details about the architecture of our proposed method are illustrated in Figure 2. The following sections explain in detail each component of the proposed method.  
It is worth noting that our proposed method is not specifically designed to work only for emotion recognition tasks or for the Arabic language; rather, it is applicable to any language and various fine-grained text classification tasks. However, the method relies on the availability, diversity, and richness of unlabeled data that can be confidently labeled during self-training; that is, if the dataset does not effectively represent the desired classes, the method's effectiveness could be restricted. Additionally, some models in our proposed method, such as the zero-shot classification model, few-shot methods, and transfer learning model, depend on the similarity between the source and target domains [56]. Therefore, if the source data utilized in pretraining the models is dissimilar to the target domain of the given task, our proposed method may not yield optimal results, limiting its applicability.  
Figure 1: General framework we proposed to boost low-resource, fine-grained emotion recognition.