## 6 Experiments
### Low-Resource Scenarios (RQ3)  
This section conducts a comparative analysis of our proposed method's performance in low-resource scenarios against the six base models. As previously explained, these base models include Naive Bayes, Logistic Regression, few-shot learning methods (i.e., nearest neighbor embedding lookup and data augmentation), a zero-shot classification model, and transfer learning (BERT-based models). We initiated the training process of our proposed method with 80 labeled examples (10 examples per emotion class) from the AETD dataset. Table 12 shows the results of our proposed method on the AETD test set at the beginning stage using only the 10 examples per emotion class without going through iterations using self-training. The table also compares our proposed method with each base model in a low-resource setting. The results show the impact of the first component of our proposed method, where a stacking ensemble model that combines different models is used on a limited amount of labeled data. We can see that our proposed method achieved the highest performance, with an accuracy of 39.96% and a weighted average F1-score of 39.47%. The second-best performing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Weighted**} & \multirow{2}{*}{**Training Data (Size)**} \\ \cline{3-3} \cline{5-6}  & & **F1** & **P** & **R** \\ \hline Zero-Shot Classification & 24.54 & 21.2 & 30.72 & 24.54 & No-training \\ \hline Naive Bayes & 58.66 & 58.56 & 59.92 & 58.66 & \\ Logistic Regression & 57.85 & 58.22 & 59.96 & 57.85 & \\ Data Augmentation & 56.91 & 56.75 & 56.12 & 56.91 & AETD (80 manually labeled examples) + \\ Embedding Lookup & 49.79 & 49.65 & 50 & 49.79 & Automatically \\ BERT & **69.02** & **69.08** & **71.02** & **69.02** & Labeled Data (22K) \\ \hline Self-training with & & & & & \\ Majority Voting & 66.41 & 67.39 & 68.97 & 65.87 & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance of self-training with majority voting on the ArPanEmo dataset. approach is the zero-shot model, which resulted in an accuracy of 37.5% and a weighted average F1-score of 33.27%. Next in line are the ML algorithms and few-shot learning methods. In the third, fourth, fifth, and sixth places are Logistic Regression, Naive Bayes, the embedding lookup method, and the data augmentation method, with weighted average F1-scores equal to 33.73%, 32.92%, 31.21%, and 21.88%, respectively. In terms of weighted average precision, the few-shot learning method, namely embedding lookup, achieved a higher result, equal to 35.58%, compared to the other few-shot learning method (i.e., data augmentation) and the other two ML algorithms (i.e., Naive Bayes and Logistic Regression). The lowest results were obtained when using transfer learning to fine-tune a model on a small amount of labeled data. The fine-tuned BERT model yielded a weighted average F1-score, precision, and recall of 11.14%, 20.09%, and 15.63%, respectively. The accuracy was also equal to 15.63%. At the end of the training (i.e., after 20 iterations of self-training), the size of the training data had expanded from 80 to 22,544 examples. At this final stage, our proposed method still achieved the highest performance in all evaluation metrics compared to other approaches. Specifically, the accuracy, weighted average F1-score, precision, and recall were all equal to 83.15%, 83.19%, 83.31%, and 83.15%, respectively. Table 13 displays the results of our proposed method and other methods at the final stage after completing the 20 iterations on the test set. The second-best performing model was not the zero-shot model, as it was in the first stage when starting with a low-resource setting (i.e., the start point with limited labeled data). Indeed, the second-best performing model in the final stage was the fine-tuned BERT-based model, which achieved an accuracy of 81.65%, along with a weighted average F1-score of 81.66%, precision of 81.73%, and recall of 81.86%.