## 4 Results
### Performance of Sentiment Analysis Modality  
Our sentiment analysis study employed a diverse array of classifiers, each with distinct parameters, structures, and fine-tuning approaches to optimize performance as shown in Table 3. We assessed various classification models, including pre-trained models, to identify potential candidates for our ensemble. We adopted the transfer learning strategy [39, 40, 41] by utilizing pre-trained language models like BERT and GPT, fine-tuning them on our target datasets: augmented EmoUERJ and ESD datasets. We augmented the ESD dataset with 300 randomly generated sentences per sentiment category (positive, neutral, negative), resulting in a total of 1250 labeled English sentences. The EmoUERJ dataset, initially limited to 10 sentences, was similarly augmented with 300 Portuguese sentences per class, yielding 910 labeled sentences. Table 12 presents the overall results across both datasets for BERT, GPT, SVM, RF, LR, MLP, 1D-CNN, and the DBMM ensemble. Tables 13 and 14 provide \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Classifier** & **Overall PREC** & **Overall REC** & **Overall FI-Score** & **Overall ACC** \\ \hline
1D-CNN & 0.88 & 0.87 & 0.88 & 0.87 \\ \hline SVM & 0.85 & 0.85 & 0.85 & 0.85 \\ \hline RF & 0.87 & 0.87 & 0.87 & 0.87 \\ \hline GNB & 0.49 & 0.47 & 0.44 & 0.46 \\ \hline MLP & 0.94 & 0.94 & 0.94 & 0.94 \\ \hline
**DBMM** & **0.98** & **0.98** & **0.98** & **0.98** \\ \hline \end{tabular}
\end{table} 
Table 10: Overall performance on multilingual dataset (EmoUERJ + ESD). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**(a) 1D-CNN** & \multicolumn{6}{c}{**(b) SVM**} \\ \hline  & **PREC** & **REC** & **F1** & **Supp** & **PREC** & **REC** & **F1** & **Supp** \\ \hline Happy & 0.95 & 0.76 & 0.81 & 727 & Happy & 0.83 & 0.82 & 0.83 & 727 \\ \hline Neutral & 0.91 & 0.79 & 0.84 & 706 & Neutral & 0.8 & 0.88 & 0.84 & 706 \\ \hline Sad & 0.78 & 0.98 & 0.86 & 762 & Sad & 0.9 & 0.83 & 0.86 & 762 \\ \hline Angry & 0.88 & 0.93 & 0.88 & 681 & Angry & 0.86 & 0.86 & 0.86 & 681 \\ \hline
**ACC** & \multicolumn{6}{c}{0.87} & 2876 & **ACC** & \multicolumn{6}{c}{0.85} & 2876 \\ \hline Macro Avg & 0.88 & 0.87 & 0.88 & 2876 & Macro Avg & 0.85 & 0.85 & 0.85 & 2876 \\ \hline Weight Avg & 0.88 & 0.87 & 0.88 & 2876 & Weight Avg & 0.85 & 0.85 & 0.85 & 2876 \\ \hline \multicolumn{6}{c}{**(c) RF**} & \multicolumn{6}{c}{**(d) GNB**} \\ \hline  & **PREC** & **REC** & **F1** & **Supp** & **PREC** & **REC** & **F1** & **Supp** \\ \hline Happy & 0.87 & 0.84 & 0.85 & 727 & Happy & 0.49 & 0.28 & 0.36 & 727 \\ \hline Neutral & 0.84 & 0.91 & 0.88 & 706 & Neutral & 0.39 & 0.86 & 0.54 & 706 \\ \hline Sad & 0.91 & 0.86 & 0.88 & 762 & Sad & 0.46 & 0.19 & 0.27 & 762 \\ \hline Angry & 0.85 & 0.87 & 0.86 & 681 & Angry & 0.62 & 0.53 & 0.57 & 681 \\ \hline
**ACC** & \multicolumn{6}{c}{0.87} & 2876 & **ACC** & \multicolumn{6}{c}{0.46} & 2876 \\ \hline Macro Avg & 0.87 & 0.87 & 0.87 & 2876 & Macro Avg & 0.49 & 0.47 & 0.44 & 2876 \\ \hline Weight Avg & 0.87 & 0.87 & 0.87 & 2876 & Weight Avg & 0.49 & 0.46 & 0.43 & 2876 \\ \hline \multicolumn{6}{c}{**(e) MLP**} & \multicolumn{6}{c}{**(f) DBMM**} \\ \hline  & **PREC** & **REC** & **F1** & **Supp** & **PREC** & **REC** & **F1** & **Supp** \\ \hline Happy & 0.94 & 0.92 & 0.93 & 727 & Happy & 0.99 & 0.99 & 0.99 & 727 \\ \hline Neutral & 0.94 & 0.96 & 0.95 & 706 & Neutral & 0.99 & 0.99 & 0.99 & 706 \\ \hline Sad & 0.97 & 0.94 & 0.96 & 762 & Sad & 0.98 & 0.97 & 0.97 & 762 \\ \hline Angry & 0.91 & 0.93 & 0.92 & 681 & Angry & 0.95 & 0.95 & 0.95 & 681 \\ \hline
**ACC** & \multicolumn{6}{c}{0.94} & 2876 & **ACC** & \multicolumn{6}{c}{0.98} & 2876 \\ \hline Macro Avg & 0.94 & 0.94 & 0.94 & 2876 & Macro Avg & 0.98 & 0.98 & 0.98 & 2876 \\ \hline Weight Avg & 0.94 & 0.94 & 0.94 & 2876 & Weight Avg & 0.98 & 0.98 & 0.98 & 2876 \\ \hline \end{tabular}
\end{table} 
Table 11: Classification results per emotion on multilingual dataset (EmoUERJ + ESD).