## 5 Model Compilation Using Deep Learning Methods
### Results and Discussion
#### 5.4.3 Error Analysis  
Here, we analyze the misclassified articles in the country-of-origin classification model for each approach used: ML or DL. We provide examples of the misclassified articles \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Technique**} & \multicolumn{4}{c}{**CNN+LSTM**} & \multicolumn{4}{c}{**CNN+BiLSTM**} & \multicolumn{4}{c}{**CNN+GRU**} \\ \cline{2-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Random Embedding & 0.94 & 0.94 & **0.94** & 0.94 & 0.94 & **0.94** & 0.88 & 0.88 \\ Word2 vec & 0.84 & 0.84 & 0.84 & 0.86 & 0.86 & **0.86** & 0.79 & 0.79 \\ FastText & 0.93 & 0.93 & **0.93** & 0.92 & 0.92 & 0.91 & 0.91 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The performance of the hybrid deep learning models using different embedding techniques for the Hajj articles dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Embedding Technique**} & \multicolumn{4}{c}{**CNN+LSTM**} & \multicolumn{4}{c}{**CNN+BiLSTM**} & \multicolumn{4}{c}{**CNN+GRU**} \\ \cline{2-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Random Embedding & **0.73** & 0.73 & 0.73 & 0.75 & 0.75 & **0.75** & 0.70 & 0.70 \\ Word2 vec & 0.59 & 0.59 & 0.59 & 0.63 & 0.63 & **0.63** & 0.54 & 0.54 \\ FastText & 0.76 & 0.76 & **0.76** & 0.71 & 0.71 & 0.66 & 0.66 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The performance of the hybrid deep learning models using different embedding techniques for the Brexit dataset. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Model**} & \multicolumn{4}{c}{**Accuracy**} \\ \cline{2-9}  & **CNN+LSTM** & **CNN+BiLSTM** & **CNN+GRU** \\ \hline Random Embedding & **94.4\%** & 93.54\% & 88.04\% \\ \hline Word2 vec & 84.21\% & **86.36\%** & 77.75\% \\ \hline FastText & **93.06\%** & 92.34\% & 91.15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy of the hybrid deep learning models for the Hajj articles dataset. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Technique**} & \multicolumn{4}{c}{**CNN+LSTM**} & \multicolumn{4}{c}{**CNN+BiLSTM**} & \multicolumn{4}{c}{**CNN+GRU**} \\ \cline{2-9}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Random Embedding & 0.94 & 0.94 & **0.94** & 0.94 & 0.94 & **0.94** & 0.88 & 0.88 \\ Word2 vec & 0.84 & 0.84 & 0.84 & 0.86 & 0.86 & **0.86** & 0.79 & 0.79 & 0.79 \\ FastText & 0.93 & 0.93 & **0.93** & 0.92 & 0.92 & 0.92 & 0.91 & 0.91 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The performance of the hybrid deep learning models using different embedding techniques for the Hajj articles dataset. (with their English translation) and propose reasons for the errors. For the ML model, we found that the linear SVC with n-gram = 1 in the Hajj dataset and n-gram = 3 with the same ML algorithm in the Brexit dataset resulted in a lower error rate. According to the experiments' confusion matrix pertaining to the Hajj dataset, we found that 138 out of 152 of the Egyptian articles in the testing set were correctly classified by the classifiers, while 10 were classified as Saudi and four as Jordanian. Moreover, 126 out of the 128 Saudi articles were classified correctly, with one misclassified as Jordanian and another as Egyptian. Likewise, 126 Jordanian articles were correctly classified, with six misclassified as Egyptian and five as Saudi (see Figure 12). On the other hand, for the Brexit dataset, more misclassified articles under the same classifier were noted. Specifically, 11 and 19 Egyptian articles were mistaken as being of Saudi and Jordanian origin, respectively, Figure 13.  
Similarly, random embedding with the hybrid model of CNN+LSTM classified 138 Egyptian articles correctly and misclassified two articles.