## 6 Experiments
### Ablation Experiments (Rq2)  
Our proposed method depends on two main components: (a) a stacking ensemble model that combines the predictions from diverse models employing various learning  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Weighted**} \\ \cline{3-5}  & & **F1** & **P** & **R** \\ \hline HF + DF [47] & 71.8 & 71.8 & 72.2 & 71.8 \\ \hline Complement Naive Bayes [39] & 68.12 & 65.8 & 68.8 & 68.1 \\ \hline Sequential Minimal & & & & \\ Optimization [39] & 63.43 & 63.7 & 64.3 & 63.4 \\ \hline BERT [57] & 78.93 & 78.93 & 78.97 & 78.97 \\ \hline BERT Integrated with Named & & & & \\ Entity Recognition and & 81.73 & 81.6 & 81.92 & 81.4 \\ Sentiment Analysis [57] & & & & \\ \hline Our Proposed Approach & **83.15** & **83.19** & **83.31** & **83.15** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparisons between previous studies on Arabic emotion recognition and our proposed approach when tested on the AETD dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Weighted**} & \multicolumn{3}{c}{**Macro**} \\ \cline{3-8}  & & **F1** & **P** & **R** & **F1** & **P** & **R** \\ \hline BERT [57] & 67 & 67 & 69 & 67 & 66 & 68 & 67 \\ \hline BiGRU [57] & 40 & 40 & 48 & 39 & 38 & 44 & 39 \\ \hline SVM [57] & 58 & 58 & 68 & 58 & 55 & 69 & 52 \\ \hline Our Proposed Approach & **72.28** & **72.12** & **72.81** & **72.28** & **70.63** & **72.47** & **69.83** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons between previous studies on Arabic emotion recognition and our proposed approach when tested on the ArPanEmo dataset.  
paradigms, and (b) self-training using majority voting among the diverse models to automatically expand the set of labeled training data, as shown in Figure 1. In order to dissect the impact of these two components and provide a deeper understanding of their individual contributions to the overall results, we conducted ablation experiments.  
In the first experiment, we examined the influence of the stacking ensemble model. We utilized the complete manually labeled training data of the benchmark datasets (i.e., a high-resource scenario). In the second experiment, we conducted an analysis of the performance of self-training with majority voting, isolating it from the stacking ensemble model. During the self-training experiments, we did not use the entire set of training labeled data and, instead, initiated the training process with only 10 labeled examples per emotion class from the benchmark datasets.  
In both ablation experiments, we developed six base classifiers, which included Naive Bayes, Logistic Regression, few-shot methods (i.e., nearest neighbor embedding lookup and data augmentation), a zero-shot classification model, and a transfer learning-based model using BERT. These diverse base classifiers are necessary for building both the stacking ensemble model and self-training with majority voting. Consequently, we also examined their performances in both settings (i.e., with stacking ensemble in a high-resource condition and with self-training using majority voting). The following subsections illustrate the results of the ablation study.