## 8 Conclusions and Future Works  
The need for specialized and nuanced classification has drawn attention to the challenge of creating fine-grained text emotion recognition models with high performance, particularly when confronted with limited labeled data. In response to this challenge, our study proposes an innovative approach that addresses low-resource, fine-grained Arabic emotion recognition. Our method entails an iterative framework comprising a stacking  
Figure 5: No. of training examples vs. F1-scores when tested on ArPanEmo.  
ensemble model and a self-training process. It eliminates the need for an extensive initial labeled dataset, progressively generating labeled data through a careful process that uses the majority voting of a set of diverse models. The stacking ensemble model learns to combine the prediction of various base models employing different learning paradigms, which helps to gain an ensemble model with better performance, exploiting the strengths of each base model. The utilized base models include few-shot models, a zero-shot classification model, machine learning models, and a BERT-based model.  
The diverse models were chosen carefully as some of them were specifically designed for low-resource data, such as the few-shot methods and zero-shot classification, while others performed well on thousands of labeled data, such as the BERT-based model and ML algorithms. The low-resource models helped our approach in the first stage of training, while the ML and BERT-based algorithms were helpful in the late stage of the iterative framework after the number of labeled training examples increased to the thousands.  
Through extensive experiments, we thoroughly assessed the individual performance of each base model for fine-grained, low-resource Arabic emotion recognition. The experimental results showed that our approach surpassed the individual performance of each base model when using a small number of labeled data. Although our approach to Arabic emotion recognition started with a small number of labeled data, it outperformed existing state-of-the-art Arabic emotion recognition models in the literature. This achievement is highlighted by our approach achieving the highest weighted average F1-scores of 83.19% and 72.12% on the AETD and ArPanEmo benchmark datasets, respectively.  
For future research, exploring various semi-supervised learning approaches, such as Expectation Maximization, Mean Teacher, and graph-based methods, could be promising alternatives to the self-training process utilized in our proposed method. In addition, examining various learning paradigms for the base models could be beneficial, including, for example, exploring alternative few-shot methods, such as prototypical networks instead of the data augmentation and embedding lookup utilized in our proposed method.  
Future research directions could also focus on proposing methods that address low-resource fine-grained text classification while reducing computational costs. Several few-shot learning methods, including data augmentation and embedding lookup, involve high computational costs, prompting the need for further investigation into cost-effective alternatives and comparisons between different methods.  
This research received no external funding.  
Not applicable.  
The datasets utilized in the experiments of this study can be found here: [[https://data.mendeley.com/datasets/d9](https://data.mendeley.com/datasets/d9) yy8w52 ns/2]([https://data.mendeley.com/datasets/d9](https://data.mendeley.com/datasets/d9) yy8w52 ns/2) (accessed at 30 July 2023) and [[https://github.com/amrmalkhatib/Emotional-Tone](https://github.com/amrmalkhatib/Emotional-Tone)]([https://github.com/amrmalkhatib/Emotional-Tone](https://github.com/amrmalkhatib/Emotional-Tone)) (accessed at 5 May 2022).  
The author declares no conflict of interest.