## 5 Experimental Results
### Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Evaluation on Test Sets: (F1 Score: Larger is Better)**} & \multirow{2}{*}{**Average**} \\  & & **Twitter** & **Reddit** & **J + T** & **T + R** & **J + R** & **J + T + R** \\ \hline Jira & **0.923** & 0.704 & 0.780 & 0.801 & 0.731 & 0.834 & 0.821 & 0.799 \\ Twitter & 0.662 & **0.931** & 0.771 & 0.832 & 0.869 & 0.726 & 0.808 & 0.799 \\ Reddit & 0.704 & 0.789 & **0.863** & 0.757 & 0.816 & 0.833 & 0.804 & 0.795 \\ J + T & 0.918 & 0.921 & 0.788 & **0.924** & 0.858 & 0.843 & 0.883 & 0.876 \\ T + R & 0.742 & 0.918 & 0.842 & 0.868 & **0.909** & 0.804 & 0.871 & 0.850 \\ J + R & 0.899 & 0.753 & 0.816 & 0.813 & 0.797 & **0.871** & 0.819 & 0.824 \\ J + T + R & 0.887 & 0.901 & 0.833 & 0.911 & 0.905 & 0.863 & **0.886** & **0.883** \\ \hline
**Average** & 0.819 & 0.845 & 0.813 & 0.844 & 0.841 & 0.825 & 0.842 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RoBERTa models with 5-class inference. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Evaluation on Test Sets: (F1 Score: Larger is Better)**} & \multirow{2}{*}{**Average**} \\  & & **Jira** & **Twitter** & **Reddit** & **J + T** & **T + R** & **J + R** & **J + T + R** \\ \hline Jira & **0.862** & 0.714 & 0.765 & 0.788 & 0.649 & 0.801 & 0.779 & 0.765 \\ Twitter & 0.759 & **0.916** & 0.782 & 0.802 & 0.840 & 0.683 & 0.780 & 0.795 \\ Reddit & 0.739 & 0.756 & **0.835** & 0.708 & 0.818 & 0.799 & 0.767 & 0.775 \\ J + T & 0.844 & 0.898 & 0.775 & **0.916** & 0.832 & 0.818 & 0.846 & 0.847 \\ T + R & 0.764 & 0.884 & 0.832 & 0.794 & **0.902** & 0.800 & 0.839 & 0.831 \\ J + R & 0.832 & 0.749 & 0.821 & 0.797 & 0.782 & **0.862** & 0.822 & 0.809 \\ J + T + R & 0.854 & 0.868 & 0.818 & 0.894 & 0.885 & 0.851 & **0.901** & **0.867** \\ \hline
**Average** & 0.808 & 0.826 & 0.804 & 0.814 & 0.815 & 0.802 & 0.819 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RoBERTa models with 4-class inference. **(3) Multi-domain models have a higher average F1 score than single-domain models, indicating a better performance. ** Based on the table, the three-domain model has the highest F1 score: 0.883 in Table 4 and 0.867 in Table 5. Moreover, all the multi-domain models achieved an average F1 score over 0.800, whereas the single-domain models scored below 0.800 on average. This is because cross-domain models were trained on data from multiple datasets, while single-domain models were fine-tuned only on their specific data collection. As a consequence, single-domain models were surpassed by multi-domain models due to decreased performance on unfamiliar domains, despite having high accuracy on self-domains. **(4) Multi-domain models are less efficient than single-domain models when evaluated on single-domain datasets. ** Cross-domain models, which are trained on data from multiple domains, might be less efficient when evaluated on single-domain datasets compared to models specifically trained on those individual domains. The reason behind this is that multi-domain models handle a wide range of data, with various linguistic styles, terminologies, or contextual nuances. This diversity is beneficial across different domains but may result in reduced performance when focused on a particular area. On the other hand, single-domain models are fine-tuned specifically for a narrow domain, which can make them yield better results when applied to datasets from that specific domain. **(5) Labels that do not fit in the dataset can affect the performance of the models. ** When comparing Tables 4 and 5, it can be observed that all scores in Table 5 are lower. The reason behind this is that certain data were labeled as neutral because they cannot be classified under a specific emotion. While having a label that can fit anywhere can improve the model's performance, it can also potentially degrade the quality of the data it was trained on.