## 4 Results and Discussion
### Experimental Findings
When comparing Word2 Vec and FastText directly, it is shown that Word2 Vec slightly outperforms FastText across both datasets in all evaluation metrics used. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**English Dataset**} & \multicolumn{4}{c}{**French Dataset**} & \multicolumn{4}{c}{**Arabic Dataset**} \\ \cline{2-13}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Word2 Vec & & & & & & & & & & & & & \\ \hline BiLSTM & 0.985 & 0.99 & 0.99 & 0.99 & 0.848 & 0.85 & 0.85 & 0.85 & 0.872 & 0.87 & 0.87 & 0.87 \\ GRU & 0.983 & 0.98 & 0.98 & 0.98 & 0.847 & 0.85 & 0.85 & 0.85 & 0.866 & 0.87 & 0.87 & 0.87 \\ BiLSTM-CNN & 0.983 & 0.98 & 0.98 & 0.98 & 0.859 & 0.86 & 0.86 & 0.86 & 0.883 & 0.89 & 0.89 & 0.89 \\ FastText & & & & & & & & & & & & \\ \hline BiLSTM & 0.984 & 0.98 & 0.98 & 0.840 & 0.84 & 0.84 & 0.84 & 0.859 & 0.86 & 0.86 & 0.86 \\ GRU & 0.980 & 0.98 & 0.98 & 0.98 & 0.830 & 0.83 & 0.83 & 0.83 & 0.862 & 0.86 & 0.86 & 0.86 \\ BiLSTM-CNN & 0.983 & 0.98 & 0.98 & 0.98 & 0.850 & 0.85 & 0.85 & 0.85 & 0.876 & 0.88 & 0.88 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: A comparison of DL algorithm performance using Word2 Vec and FastText. This variation in results between the two languages can be attributed to several factors. Firstly, the higher linguistic complexity of the French language compared to English enables models like BERT, which are context- and syntax-sensitive, to better capture these subtleties. Secondly, tasks requiring the identification of individual word importance, such as sentiment classification, allow statistical techniques like TF-IDF to achieve high precision. Also, the dataset size significantly influences model performance, with word embedding models excelling on larger datasets due to their ability to learn lexical and contextual similarities more effectively than on smaller datasets. Building on the previous comparison of French and English datasets, the performance of the best models from each vectorization technique is compared. The results on the Arabic dataset, as shown in Figure 10, reveal that TF-IDF and BERT embeddings outperform word embedding models, achieving over 92% in both accuracy and F1-score metrics. In contrast, Word2 Vec demonstrates higher performance compared to FastText. The superior performance of BERT and TF-IDF can be attributed to the morphological richness, dialectal variations, and contextual complexity of the Arabic language, which make these techniques more effective than others. The models of each feature extraction were evaluated using the ROC AUC metric to measure how well they could distinguish between positive and negative classes, as shown in Figures 11 and 12. The combination of TF-IDF and the EC classifier achieves the highest AUC scores across all three datasets, particularly excelling on the English dataset with an almost perfect score of 1.0. Other models also demonstrated strong performance, consistently exceeding an AUC of 0.9, demonstrating their ability to effectively discriminate between classes. \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**English Dataset**} & \multicolumn{4}{c}{**French Dataset**} & \multicolumn{4}{c}{**Arabic Dataset**} \\ \cline{2-11}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline BERT & & & & & & & & & & & & \\ \hline BiLSTM & 0.893 & 0.890 & 0.890 & 0.890 & 0.870 & 0.870 & 0.870 & 0.870 & 0.911 & 0.920 & 0.910 & 0.910 \\ GRU & 0.892 & 0.890 & 0.890 & 0.890 & 0.868 & 0.870 & 0.870 & 0.870 & 0.906 & 0.910 & 0.910 & 0.910 \\ BiLSTM-CNN & 0.891 & 0.890 & 0.890 & 0.890 & 0.871 & 0.880 & 0.870 & 0.870 & 0.920 & 0.920 & 0.920 \\ \hline \hline \end{tabular}
\end{table}