## 4 Model Compilation Using Machine Learning Methods
### Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **N-Gram** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LinearSVC & \(1\) & **93.10\%** & **93.10\%** & **93.10\%** & **93.00\%** \\ SVM & \(1\) & 91.40\% & 91.40\% & 91.40\% & 91.30\% \\ MultinomialNB & \(1\) & 89.50\% & 90.60\% & 89.50\% & 89.50\% \\ BernoulliNB & \(1\) & 87.30\% & 88.60\% & 87.30\% & 87.50\% \\ SGDClassifier & \(1\) & 92.80\% & 92.80\% & 92.80\% & 92.80\% \\ DecisionTreeClassifier & \(1\) & 74.40\% & 78.20\% & 74.40\% & 74.50\% \\ RandomForestClassifier & \(1\) & 35.20\% & 78.30\% & 35.20\% & 21.40\% \\ KNeighborsClassifier KNN & \(1\) & 85.20\% & 86.20\% & 85.20\% & 85.10\% \\ \hline LinearSVC & \(2\) & **91.40\%** & **91.50\%** & **91.40\%** & **91.40\%** \\ SVM & \(2\) & 91.40\% & 91.50\% & 91.40\% & 91.30\% \\ MultinomialNB & \(2\) & 89.70\% & 91.30\% & 89.70\% & 89.80\% \\ BernoulliNB & \(2\) & 81.10\% & 86.40\% & 81.10\% & 81.50\% \\ SGDClassifier & \(2\) & 91.90\% & 92.00\% & 91.90\% & 91.80\% \\ DecisionTreeClassifier & \(2\) & 74.90\% & 79.90\% & 74.90\% & 75.00\% \\ RandomForestClassifier & \(2\) & 32.50\% & 70.20\% & 32.50\% & 17.30\% \\ KNeighborsClassifier KNN & \(2\) & 84.70\% & 86.00\% & 84.70\% & 84.70\% \\ \hline LinearSVC & \(3\) & **91.40\%** & **91.60\%** & **91.40\%** & **91.40\%** \\ SVM & \(3\) & **91.10\%** & **91.40\%** & **91.10\%** & **91.10\%** \\ MultinomialNB & \(3\) & 89.20\% & 90.90\% & 89.20\% & 89.30\% \\ BernoulliNB & \(3\) & 72.50\% & 82.50\% & 72.50\% & 72.80\% \\ SGDClassifier & \(3\) & 91.60\% & 91.90\% & 91.60\% & 91.60\% \\ DecisionTreeClassifier & \(3\) & 74.60\% & 79.60\% & 74.60\% & 74.80\% \\ RandomForestClassifier & \(3\) & 35.90\% & 75.80\% & 35.90\% & 22.70\% \\ KNeighborsClassifier KNN & \(3\) & 85.40\% & 86.60\% & 85.40\% & 85.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of different machine learning classifiers for the Hajj articles dataset. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **N-Gram** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LinearSVC & \(1\) & **78.60\%** & **80.10\%** & **78.60\%** & **79.00\%** \\ SVC & \(1\) & 77.20\% & 80.00\% & 77.20\% & 77.90\% \\ MultinomialNB & \(1\) & 74.80\% & 77.20\% & 74.80\% & 74.20\% \\ BernoulliNB & \(1\) & 62.90\% & 71.80\% & 62.90\% & 61.80\% \\ SGDClassifier & \(1\) & 78.20\% & 79.20\% & 78.20\% & 78.60\% \\ DecisionTree & \(1\) & 73.80\% & 76.40\% & 73.80\% & 74.30\% \\ RandomForest & \(1\) & 36.10\% & 41.10\% & 36.10\% & 28.20\% \\ KNeighbors & \(1\) & 65.00\% & 65.40\% & 65.00\% & 64.00\% \\ \hline LinearSVC & \(2\) & **82.00\%** & **82.60\%** & **82.00\%** & **82.20\%** \\ SVC & \(2\) & 77.20\% & 81.20\% & 77.20\% & 77.90\% \\ MultinomialNB & \(2\) & 74.10\% & 76.00\% & 74.10\% & 73.40\% \\ BernoulliNB & \(2\) & 53.70\% & 70.50\% & 53.70\% & 51.00\% \\ SGDClassifier & \(2\) & **81.00\%** & **81.90\%** & **81.00\%** & **81.30\%** \\ DecisionTree & \(2\) & 77.20\% & 78.80\% & 77.20\% & 77.50\% \\ RandomForest & \(2\) & 31.60\% & 44.00\% & 31.60\% & 16.10\% \\ KNeighbors & \(2\) & 64.60\% & 65.10\% & 64.60\% & 63.60\% \\ \hline LinearSVC & \(3\) & **83.00\%** & **83.70\%** & **83.00\%** & **83.20\%** \\ SVC & \(3\) & 75.20\% & 80.10\% & 75.20\% & 75.90\% \\ MultinomialNB & \(3\) & 74.10\% & 75.50\% & 74.10\% & 73.60\% \\ BernoulliNB & \(3\) & 47.60\% & 68.10\% & 47.60\% & 42.00\% \\ SGDClassifier & \(3\) & **82.30\%** & **82.90\%** & **82.30\%** & **82.50\%** \\ DecisionTree & \(3\) & 75.50\% & 77.70\% & 75.50\% & 75.80\% \\ RandomForest & \(3\) & 31.60\% & 78.70\% & 31.60\% & 16.10\% \\ KNeighbors & \(3\) & 63.60\% & 63.90\% & 63.60\% & 62.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance of different machine learning classifiers for the Brexit dataset. Table 5 confirmed the previous results, as both linear SVC and SGD classifiers achieved the best performance across all the experimental models. However, the best accuracy was achieved at n-gram = 3.