## 3 Results
### Model's Performance After Testing in an External Dataset  
After training our model with a small portion of the data (10% or 285 entries), we saved the trained model and then we introduced a portion of the data unseen by the model during the training. Even though we used a testing-training split during the training to evaluate the model's performance, a model trained with a smaller portion of data and then tested with a large portion of unseen data is crucial to ensure overfitting is avoided and a good model evaluation in generalization. The model showcased exemplary performance in the new dataset (90% of the data or 2573 entries), further ensuring its robustness, generalizability, and ability for real-world scenario applications, as shown in Table 7.  
Performance of the Trained Model in the Test Set (External Dataset)  
The trained model's overall performance exhibits a very high score, and the trained model predicts correctly the data instances, with a good ability to distinguish positive and negative classes. The curve shape pinpoints that the model achieves a high true-positive rate and, at the same time, maintains a low number of false positives. A minor score drop is observed, and it is acceptable considering the fact that the model was trained on a very small portion of data with the class imbalance and yet maintains its reliability [48; 49] Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Balanced Accuracy in the External Dataset** & 0.8667 \\ \hline
**ROC-AUC Score:** & 0.95 \\ \hline
**Precision:** & 0.87 \\ \hline
**Recall:** & 0.87 \\ \hline
**F1-Score:** & 0.87 \\ \hline
**Accuracy:** & 0.87 \\ \hline
**Positive Predictive Values:** & 0.88 \\ \hline
**Negative Predictive Values:** & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model’s evaluation metrics after the testing. **Balanced Accuracy in the External Dataset:** The score of 0.88 suggests that the model is generalizing well to the unseen data, but at the same time, some noise or feature variability may have been introduced, therefore, causing the drop in the score. **ROC-AUC Score:** The score of 0.95 shows that although the balanced accuracy dropped, the model's overall ability for predictions remains reliable. **Precision:** The score of 0.87 means that the model predicts correct positive classes 87% of the time. **Recall:** The score of 0.87 means that the model successfully identifies 87% of the actual positive cases. **F1-Score:** The score of 0.87 indicates that the model has both high sensitivity and specificity. **Accuracy:** The score of 0.87 shows that the model performs well in the overall classification. Consistency with other metrics is shown, where the F1-, precision, and recall scores are aligned, reinforcing the reliability of the model. **Positive Predictive Value (PPV):** The score of 0.88 shows that most of the positive predictions are correct. **Negative Predictive Value (NPV):** The score of 0.85 shows that the model is predicting negative instances correctly 85% of the time. Confusion Matrix Figure 6  
**True Positives (TPs):** 1327--Instances of the positive class correctly identified by the model. **True Negatives (TNs):** 914--Instances of the negative class correctly identified by the model. **False Positives (FPs):** 175--Instances of the negative class incorrectly identified by the model. **False Negatives (FNs):** 157--Instances of the positive class incorrectly identified by the model. **Accuracy for Aggression Detections:** 90%--From all the true labels as aggressive, the model identified 90% of them as actually aggressive. **Accuracy for Argument Detections:** 84%--From all the true labels as non-aggressive, the model identified 84% of them as actually non-aggressive. Figure 5: ROC-AUC score of the model after testing. The performance of the model tested in an external dataset shows promising results, showcasing a good prediction balance between the two classes falling between the 0.8 and 0.9 accuracy values.