## 4 Results
### Performance of Sentiment Analysis Modality
detailed per-class performance metrics (precision, recall, F1-score, and accuracy) for the ESD and EmoUERJ datasets, respectively. * **Individual Classifier Performance**: BERT consistently performed strongly, outperforming other individual classifiers. This demonstrates the effectiveness of pre-trained language models in capturing complex linguistic patterns and contextual information. On the ESD dataset, BERT excelled, achieving high precision, recall, and accuracy across all sentiment classes, particularly with 91% accuracy for positive sentiment. SVM (75%) and random forest (76%) also showed competitive performance, especially in classifying negative sentiment. On the EmoUERJ dataset, BERT maintained strong performance, particularly in recognizing negative sentiment. However, it performed comparatively lower on positive sentiment (80%). Classical models like SVM (75%) and LR (72%) displayed more stable performance across sentiment classes. * **Ensemble Performance**: For both datasets, the DBMM ensemble model, combining the top-performing classifiers (BERT, SVM, and RF for ESD; BERT, LR, and SVM for EmoUERJ), significantly outperformed individual models, achieving accuracies of 95% and 85%, respectively. This highlights the effectiveness of ensemble methods in mitigating text language and dataset-specific challenges. * **Discussion**: The results underscore the efficacy of ensemble methods like the DBMM in sentiment analysis, aligning with previous research [42]. Pre-trained models like BERT also proved highly effective, especially when fine-tuned on domain-specific data. The superior performance on the ESD dataset compared to EmoUERJ likely stems from the former's larger size and diversity, as well as the abundance of pre-trained models available for English language. Our approach of augmenting the datasets with randomly generated sentences was also beneficial, particularly for the smaller EmoUERJ dataset. The results suggest that combining different types of classifiers in an ensemble can significantly enhance performance, as the strengths of each model can compensate for the weaknesses of others. Overall, our study demonstrates the importance of dataset diversity and the power of ensemble methods in achieving high accuracy and robustness in sentiment analysis. The findings also highlight the potential benefits of leveraging pre-trained language models and data augmentation techniques for improving sentiment analysis in low-resource language contexts. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**(a) Augmented ESD Dataset**} \\ \hline
**Classifier** & **Overall FREC** & **Overall REC** & **Overall F1-Score** & **Overall ACC** \\ \hline BERT & 0.9 & 0.93 & 0.92 & 0.91 \\ \hline GPT-2 & 0.71 & 0.68 & 0.69 & 0.71 \\ \hline SVM & 0.78 & 0.69 & 0.73 & 0.75 \\ \hline RF & 0.85 & 0.69 & 0.76 & 0.76 \\ \hline LR & 0.77 & 0.67 & 0.72 & 0.74 \\ \hline MLP & 0.7 & 0.66 & 0.68 & 0.7 \\ \hline
1D-CNN & 0.64 & 0.59 & 0.61 & 0.65 \\ \hline
**DBMM** & **0.93** & **0.96** & **0.94** & **0.95** \\ \hline \multicolumn{5}{c}{**(b) Augmented EmoUERJ Dataset**} \\ \hline
**Classifier** & **Overall PREC** & **Overall REC** & **Overall F1-Score** & **Overall ACC** \\ \hline BERT & 0.81 & 0.81 & 0.81 & 0.81 \\ \hline GPT-2 & 0.59 & 0.56 & 0.57 & 0.53 \\ \hline SVM & 0.75 & 0.73 & 0.74 & 0.75 \\ \hline RF & 0.6 & 0.62 & 0.61 & 0.64 \\ \hline LR & 0.74 & 0.71 & 0.72 & 0.72 \\ \hline MLP & 0.69 & 0.68 & 0.68 & 0.69 \\ \hline
1D-CNN & 0.43 & 0.46 & 0.44 & 0.36 \\ \hline
**DBMM** & **0.85** & **0.85** & **0.85** & **0.85** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Overall results of sentiment analysis on ESD and EmoUERJ datasets.