## 4 Results and Discussion
### Experimental Findings  
In this study, the datasets are divided into separate portions in accordance with the requirements of both machine learning and deep learning models as presented in Table 5. For machine learning classifiers, each dataset is split into 80% for training and 20% for testing. For the deep learning algorithms, however, the division varies by dataset: the English dataset is divided into 80% for training, with 20% of the training set reserved for validation, and 20% for testing. The French dataset uses 85% for training, including 10% for validation, and 15% for testing. And the Arabic dataset uses 80% for training, including 10% for validation, and 20% for testing. The hyperparameters used in this study are influenced by various factors, such as the size of the dataset and the nature of the language. Different parameters are employed for machine learning classifiers, as shown in Table 6, like the kernel for SVM and the number of estimators for RF. For deep learning algorithms, the hyperparameters are tuned for each configuration, such as the learning rate and batch size, as presented in Table 7. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**English Dataset**} & \multicolumn{3}{c}{**French Dataset**} & \multicolumn{3}{c}{**Arabic Dataset**} \\ \cline{2-10}  & **Training** & **Validation** & **Testing** & **Training** & **Validation** & **Testing** & **Training** & **Validation** & **Testing** \\ \hline ML classifiers & 70,911 & - & 17,727 & 10,084 & - & 2520 & 4298 & - & 1074 \\ DL Classifiers & 56,729 & 14,182 & 17,727 & 9662 & 1074 & 1890 & 3868 & 430 & 1074 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dataset splitting (training, validation, and testing sets). Our research proposes a new approach to comparison between four feature extraction techniques combined with three machine learning and deep learning algorithms, applying three different datasets to improve the effectiveness of statistical methods without context (TF-IDF), methods based on similarity of context with fixed words (Word2 Vec and FastText) and methods based on global and dynamic context (BERT embedding). Table 8 presents the results of combining TF-IDF with SVM, RF, and EC classifiers. The EC model achieved the highest accuracy in both the English and Arabic datasets, with 98.6% and 94.1%, respectively, outperforming the other classifiers. For the French dataset, the SVM and EC classifiers demonstrated strong performance, achieving accuracies of 98.3% and 89.2%, respectively, with both models displaying a performance according to the precision metric that reaches 90%. The RF model showed the lowest performance across all three datasets, with accuracies of 96.2% for the English dataset, 93.3% for the Arabic dataset, and 88.8% for the French dataset. These results highlight the effectiveness of combining TF-IDF with a stacking ensemble learning approach, allowing different base models to leverage these features to capture diverse aspects and complex relationships in textual data, thereby achieving improved generalization compared to individual classifiers. As the English dataset was labeled using TextBlob, some alignment with TF-IDF-based models may exist. This does not affect the validity of our results but suggests that human-annotated data could offer a complementary evaluation perspective. Furthermore, the sentiment labels in the French and Arabic datasets were manually reviewed and showed strong face validity, reinforcing the overall reliability of the results. Table 9 evaluates the performance of two word embedding models, Word2 Vec and FastText, in conjunction with various deep learning algorithms. On the English dataset,