## 4 Results and Discussion
### Experimental Findings
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifiers** & & **English Dataset** & **French Dataset** & **Arabic Dataset** \\ \hline \multirow{4}{*}{SVM} & C & 1 & 10 & 10 \\  & Kernel & RBF & RBF & RBF \\  & Degree, Gamma & 3.1 & 2.1 & 2.1 \\  & N\_estimators & 200 & 200 & 200 \\  & min\_samples\_split & 5 & 5 & 2 \\  & SVM & C = 10; Kernel = RBF; Gamma = 1 \\ EC & KNN & N estimators = 200; min samples split = 5 \\  & MLP & Hidden layers = 50; max iterations = 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of machine learning classifiers. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Learning rate & \(1\times 10^{-3}\); \(5\times 10^{-4}\); \(1\times 10^{-4}\) \\ Epoch & 10 \\ Optimizer & Adam; RMSprop \\ Batch size & 64;32;16 \\ Activation & Relu; Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hyperparameters of DL algorithms. \begin{table}
\begin{tabular}{l l} \hline \hline
**Models** & **English Dataset** & **French Dataset** & **Arabic Dataset** \\ \hline
**Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline TF-IDF & & & & & \\ \hline SVM & 0.978 & 0.98 & 0.98 & 0.893 & 0.90 & 0.89 & 0.938 & 0.94 & 0.94 & 0.94 \\ RF & 0.962 & 0.96 & 0.96 & 0.888 & 0.89 & 0.89 & 0.89 & 0.933 &  
the BiLSTM model achieved the highest accuracy and F1-score across both embedding techniques, exceeding 98.4% and 99%, respectively. Conversely, the hybrid BiLSTM-CNN model exhibited better compatibility with the embedding models compared to individual algorithms. For the French dataset, the frWac2 Vec model delivered superior results with an accuracy of 85.9%, while for the Arabic dataset, ArWordVec achieved an accuracy of 88.3%. In contrast, the FastText model recorded an accuracy of 85% and 87.6% for the French and Arabic datasets, respectively. The study shows that in all three datasets, the Word2 Vec approach performed better than the FastText model. This superior performance is likely attributed to the datasets being well cleaned, standardized, and balanced, enabling Word2 Vec to effectively capture global relationships between words and the sentiments they express. BERT, as an advanced word embedding technique, enhances its performance by capturing semantic and contextual word meanings. It was applied to our three datasets, as presented in Table 10. When used as a vectorization method for several deep learning algorithms, BERT embeddings demonstrated superior performance. In the Arabic dataset, the BiLSTM-CNN hybrid approach achieved the highest accuracy of 92%. Similarly, this hybrid approach outperformed in the French dataset, reaching an accuracy of over 87%. For the English dataset, the BiLSTM algorithm delivered the best results among all tested models, achieving an accuracy of 89.3%. In contrast, GRU algorithms showed the lowest overall performance across the three datasets, with average accuracies not exceeding 89%. Although we did not evaluate the CNN component in isolation as we did with BiLSTM, thus presenting a partial ablation, the comparison with the standalone BiLSTM model shows that the hybrid BiLSTM-CNN configuration consistently outperforms it in both the Arabic and French datasets. This suggests that the CNN layer contributes additional local pattern recognition that complements the sequential modeling capability of BiLSTM. Referring to Figure 9, a comparison is presented between the best-performing models of each feature extraction technique, achieving the highest accuracies, as detailed in Tables 8-10, for English and French datasets across various feature extraction techniques. The TF-IDF combined with EC demonstrates the highest performance compared to Word2 Vec, FastText, and BERT for both datasets. However, while BERT embeddings outperform Word2 Vec and FastText on the French dataset, the reverse is observed for the English dataset, where Word2 Vec and FastText show superior performance compared to BERT embeddings.