## 4 Experiments and Results
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Dataset** & **Method** & **Multi-Acc.** & **Jac-Acc.** & **F1-Micro** & **F1-Macro** & **LRAP** & **H-Loss** \\ \hline \multirow{8}{*}{**SenWave**} & \multirow{2}{*}{**SenWave**} & **Baseline** & 0.888 & 0.470 & 0.456 & 0.288 & 0.647 & 0.104 \\  & & & **AraBERT** & 0.925 & 0.586 & 0.629 & **0.507** & 0.633 & 0.075 \\  & & & **MARBERT** & **0.933** & **0.624** & **0.662** & 0.453 & **0.669** & **0.067** \\ \cline{2-10}  & \multirow{2}{*}{**SenAIT**} & **Baseline** & 0.886 & 0.508 & 0.545 & 0.384 & 0.601 & 0.093 \\  & & & **AraBERT** & 0.931 & 0.591 & 0.648 & **0.484** & 0.640 & **0.069** \\  & & & **MARBERT** & **0.933** & **0.630** & **0.669** & 0.483 & **0.674** & **0.067** \\ \cline{2-10}  & \multirow{2}{*}{**SenWave**} & **Baseline** & 0.892 & 0.476 & 0.502 & 0.289 & 0.655 & 0.095 \\  & & & **AraBERT** & 0.926 & 0.601 & 0.641 & 0.521 & 0.633 & 0.073 \\  & & & **MARBERT** & **0.934** & **0.633** & **0.664** & **0.523** & **0.676** & **0.068** \\ \cline{2-10}  & \multirow{2}{*}{**SenAIT**} & **Baseline** & 0.887 & 0.511 & 0.552 & 0.391 & 0.681 & 0.984 \\  & & & **AraBERT** & **0.932** & 0.622 & 0.663 & **0.565** & 0.668 & 0.067 \\  & & & **MARBERT** & **0.932** & **0.631** & **0.692** & 0.512 & **0.676** & **0.066** \\ \hline \multirow{8}{*}{**SenWave**} & \multirow{2}{*}{**SenWave**} & **Baseline** & 0.886 & 0.456 & 0.454 & 0.354 & 0.645 & 0.166 \\  & & & **AraBERT** & 0.925 & 0.586 & 0.629 & 0.507 & 0.633 & 0.075 \\  & & & **MARBERT** & **0.929** & **0.627** & **0.657** & **0.519** & **0.670** & 0.071 \\ \cline{2-10}  & \multirow{2}{*}{**SenAIT**} & **Baseline** & 0.888 & 0.460 & 0.465 & 0.396 & 0.657 & 0.171 \\  & & & **AraBERT** & **0.931** & 0.591 & 0.648 & 0.484 & 0.640 & **0.069** \\  & & & **MARBERT** & 0.930 & **0.632** & **0.671** & **0.568** & **0.673** & 0.070 \\ \cline{2-10}  & \multirow{2}{*}{**SenWave**} & **Baseline** & 0.886 & 0.455 & 0.461 & 0.350 & 0.638 & 0.212 \\  & & & **AraBERT** & 0.929 & 0.622 & 0.658 & **0.536** & 0.664 & **0.071** \\  & & & **MARBERT** & **0.931** & **0.638** & **0.665** & 0.530 & **0.680** & 0.069 \\ \cline{2-10}  & \multirow{2}{*}{**SenAIT**} & **Baseline** & 0.887 & 0.477 & 0.480 & 0.400 & 0.678 & 0.163 \\ \cline{2-10}  & & & **AraBERT** & **0.936** & **0.651** & 0.694 & 0.632 & **0.691** & **0.064** \\ \cline{2-10}  & & & **[PERSON]** & 0.932 & 0.636 & **0.725** & **0.704** & 0.678 & 0.068 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the proposed models. dialect Arabic and Modern Standard Arabic (MSA). Thus, enabling MARBERT to capture Dialectal Arabic used with the majority of tweets, whereas AraBERT was trained only on MSA. Enriching the minority classes of the SenWave dataset with samples from AIT dataset improved the performance of the proposed models sharply as can be seen in Table 3. All models have been trained and evaluated on the SenAIT dataset resulting from this integration. As shown in the table, the improvement of F1-Micro ranges from around 1.5% to 8.9% and it can be seen with almost all experiment's arguments (emojis and DWLF). Emojis play a significant role in expressing sentiments that cannot be expressed textually. As shown in Table 3, replacing the emojis with their textual description improved the performance across all models and datasets. For example, the F1-Micro increased by a substantial margin, 0.07-5.4%, compared with the performance of the same models without emojis. The highest improvement of F1-Micro is around 5.4% with the MARBERT model. This indicates that emojis information is complementary to the textual content, and modeling emojis by their meaning is important for emotion detection. Although SenAIT helped in improving the performance of the proposed models, this dataset still has the issue of imbalanced classes, as discussed in Section 3.5.