## 6 Experiments
### Low-Resource Scenarios (RQ3)
On the other hand, in the final stage, the zero-shot model ranked last, with an accuracy of 34.03% and a weighted average F1-score, precision, and recall of 30.93%, 42.67%, and 34.03%, respectively. The performance of ML algorithms in the final stage was enhanced. Naive Bayes and Logistic Regression achieved weighted average F1-scores of 68.94% and 63.63%, respectively, showing an increase of 36.02% and 29.9% from the first stage. The few-shot models also demonstrated improvement after 20 iterations compared to the results they obtained in the first stage, achieving a weighted average F1-score of 58.28% and 63.56% for the embedding lookup and data augmentation methods, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Weighted**} & \multicolumn{3}{c}{**Macro**} & \multicolumn{3}{c}{**Micro**} \\ \cline{3-11}  & & & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** \\ \hline \begin{tabular}{c} Zero-Shot \\ Classification \\ \end{tabular} & \begin{tabular}{c} XLM-RoBERTa- \\ large-xmli model. \\ \end{tabular} & 37.5 & 33.27 & 55.47 & 37.5 & 33.27 & 55.47 & 37.5 & 37.5 & 37.5 & 37.5 \\ \hline \multirow{2}{*}{Machine Learning} & \begin{tabular}{c} Nive Bayes (NB) \\ Logistic Regression (LR) \\ \end{tabular} & 34.18 & 32.92 & 33.39 & 34.18 & 32.74 & 33.31 & 33.94 & 34.18 & 34.18 & 34.18 \\ \multirow{2}{*}{Few-Shot Learning} & \begin{tabular}{c} Data Augmentation \\ Embedding Lookup \\ \end{tabular} & 25 & 21.88 & 21.54 & 25 & 19.84 & 18.91 & 23.7 & 25 & 25 & 25 \\ \multirow{2}{*}{\begin{tabular}{c} Transfer \\ Learning \\ \end{tabular} } & \
\begin{tabular}{c} Data Augmentation \\ Embedding Lookup \\ \end{tabular} & 33.75 & 31.21 & 35.58 & 33.75 & 29.91 & 33.28 & 33.33 & 33.75 & 33.75 & 33.75 \\ \hline
\begin{tabular}{c} Transfer \\ Learning \\ \end{tabular} & BERT & 15.63 & 11.14 & 20.09 & 15.63 & 10.93 & 21.07 & 15.54 & 15.63 & 15.63 & 15.63 \\ \hline \multicolumn{1}{c}{Our Proposed Approach} & **39.96** & **39.47** & **40.12** & **39.96** & **39.45** & **40.45** & **39.58** & **39.96** & **39.96** \\ \hline \end{tabular}
\end{table} 
Table 12: Performance results of models on the AETD dataset when trained using a small amount of labeled data (80 examples from AETD). Regarding the ArPanEmo dataset, all methods, including our proposed approach, were trained on a small number of labeled data (10 examples per class, resulting in 110 labeled examples from ArPanEmo). At this stage, no iterations were applied. Table 14 shows the results of our proposed method and the other methods when trained on a small number of labeled data. Our proposed method achieved the highest performance, with an accuracy of 31.53%. The weighted average F1-score, precision, and recall were 31.24%, 31.47%, and 31.53%, respectively. The second-best performing model was the zero-shot classification method, with an accuracy of 26.36%. The data augmentation method ranked third, with an accuracy of 25.04% and a weighted average F1-score of 24.42%. The model with the lowest performance when trained on a small number of labeled data was the fine-tuned BERT-based model, which achieved an accuracy of 14.03% and a weighted average F1-score of 9.15%. Increasing the number of labeled training examples using self-training improved the overall performance of all models. At the final stage of training, when reaching the 20 th iteration, the number of training examples increased to 21,135 labeled examples. The most significant positive impact was on the performance of our proposed approach, with accuracy increasing to 72.28%, while the weighted average F1-score, precision, and recall were 72.12%, 72.81%, and 72.28%, respectively. Table 15 displays the performance results of our proposed model on a large amount of automatically labeled data, as well as the results of the other models.