## 4 Experimental Analysis
### Presentation of the Result of Training Models  
In the initial evaluation using the open data source dataset, all models achieved moderate performance, with noticeable variation in recall (see Table 5). Extra Trees yielded the highest accuracy (89.82%), but this was coupled with a relatively low recall (68.54%) and an F1-score of 0.7727, suggesting the model leaned toward conservative classifications with a high number of true negatives. In contrast, XGBoost demonstrated the strongest F1-score at 0.8306, reflecting a more balanced trade-off between precision (0.8954) and recall (0.7745). Random forest followed closely, offering a comparable balance (F1-score: 0.7810). These results indicate that while Extra Trees appeared superior by accuracy alone, XGBoost provided the most reliable performance for practical detection in this domain. To address the overall performance deficit and facilitate improvement, a fine-tuning of model depth was conducted to accurately detect instances of terrorism-related terms. Table 6 summarizes the hyperparameter tuning process conducted on machine learning models using the open data source dataset. For each model, including Decision Tree, Bagging, XGBoost, random forest, and Extra Trees, specific hyperparameters were adjusted across defined value ranges. The best-performing configurations were selected based on improvements in model accuracy, precision, recall, and F1-score. For instance, setting the max_depth to 15 and using entropy as the splitting criterion significantly improved \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **ROC Area** \\ \hline Decision Tree & 0.8711 & 0.8756 & 0.6284 & 0.7317 & 0.7563 \\ \hline Bootstrap Aggregating & 0.8363 & 0.8825 & 0.6756 & 0.7653 & 0.7246 \\ \hline XGBoost & 0.8591 & 0.8954 & 0.7745 & 0.8306 & 0.7458 \\ \hline Random Forest & 0.8656 & 0.8874 & 0.6974 & 0.7810 & 0.7624 \\ \hline Extra Trees & 0.8982 & 0.8854 & 0.6854 & 0.7727 & 0.7714 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The model training performance of open data source. the decision tree model's sensitivity, while increasing the number of estimators in ensemble methods like Bagging, random forest, and Extra Trees enhanced generalization. The adjustments made to these hyperparameters demonstrated clear performance gains over the default settings and helped achieve a more balanced and reliable detection of terrorism-related terms from open text sources. The fine-tuning has resulted in notable enhancements in all models, especially regarding accuracy, precision, and recall (refer to Table 7). Following hyperparameter tuning, all models showed improved performance across all metrics. Notably, Extra Trees again achieved the highest accuracy (94.31%), but its recall (71.97%) remained lower than that of XGBoost (81.32%). When recalculated correctly, XGBoost reached the highest F1-score (0.8707), outperforming all other models, including random forest (F1-score: 0.8202). This underscores the effectiveness of boosting techniques in capturing nuanced lexical features in unstructured text. Although Extra Trees retained strong precision (92.97%) and accuracy, its relative weakness in recall suggests that it may be less effective in identifying all relevant terrorism-related terms, particularly in ambiguous cases.