## 4 Experiments Results
### Results
#### 4.4.1 The Performance Results of Models for Main-AHS Dataset  
This section presents the performance results of three approaches applied to the Main-AHS dataset. The first approach is the ML models; RF, DT, LR, and KNN were applied with TF-IDF and Unigram, bi-gram. The second approach is the DL models, DeepCNN, CNN-LSTM, and CNN-GRU were applied with SkipGram and CBOW word embedding. The third approach is the proposed model. Table 4 shows the values of four metrics, including accuracy, precision, recall, and f1-score of the testing results for three approaches. For ML models, LR with unigram had the highest performance (85.96% for accuracy, 86.41% for precision, 85.96% for recall, 85.13% for f1-score), while KNN with Bi-gram had the lowest (68.97% for accuracy, 47.56% for precision, 68.97% for recall, and 56.3% for f1-score). RF with unigram registered the second-highest result (83.74% for accuracy, 84.43% for precision, 83.74% for recall, and 82.47% for f1-score). DL models showed an improvement in terms of accuracy, precision, recall, and f1-score when CBOW word embedding was applied. CNN-LSTM with CBOW achieved the highest performance (91.38% for accuracy, 91.31% for precision, 91.38% for recall, and 91.27% for f1-score), and it improved accuracy by 5.42%, precision by 4.9%, recall by 5.42%, and f1-score by 6.14% compared to LR with the unigram model. DeepCNN with SkipGram produced the lowest performance: 89.41% for accuracy, 89.49% for precision, 89.41% for recall, and 89.44% for f1-score). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Feature Extraction Method**} & \multicolumn{5}{c}{**Testing Performance**} \\ \cline{3-6}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{ML approach} & \multirow{3}{*}{KNN} & Unigram & 82.02 & 83.1 & 82.02 & 80.22 \\ \cline{3-6}  & & Bi-gram & 68.97 & 47.56 & 68.97 & 56.3 \\ \cline{2-6}  & & Unigram & 79.56 & 79.35 & 79.56 & 79.44 \\ \cline{2-6}  & & Bi-gram & 77.09 & 76.25 & 77.09 & 75.25 \\ \cline{2-6}  & & Unigram & 85.96 & 86.41 & 85.96 & 85.13 \\ \cline{2-6}  & & Bi-gram & 78.33 & 77.63 & 78.33 & 76.82 \\ \cline{2-6}  & & Unigram & 82.02 & 85.29 & 82.02 & 79.5 \\ \cline{2-6}  & & Bi-gram & 82.02 & 82.38 & 82.02 & 80.57 \\ \cline{2-6}  & & Unigram & 83.74 & 84.43 & 83.74 & 82.47 \\ \cline{2-6}  & & Bi-gram & 78.08 & 78.32 & 78.08 & 75.53 \\ \hline \multirow{8}{*}{DL approach} & Deep CNN & & 89.41 & 89.49 & 89.41 & 89.44 \\ \cline{2-6}  & CNN-LSTM & SkipGram & 90.38 & 90.36 & 90.38 & 90.37 \\ \cline{1-1} \cline{2-6}  & CNN-GRU & & 90.89 & 91.08 & 90.89 & 90.95 \\ \cline{1-1} \cline{2-6}  & Deep CNN & & 90.64 & 90.74 & 90.64 & 90.68 \\ \cline{1-1} \cline{2-6}  & CNN-LSTM & CBOW & 91.38 & 91.31 & 91.38 & 91.27 \\ \cline{1-1} \cline{2-6}  & CNN-GRU & & 91.02 & 91.02 & 91.02 & 91.02 \\ \hline \multirow{2}{*}{The proposed ensemble model} & Stacking LR & SkipGram & 91.63 & 91.56 & 91.63 & 91.57 \\ \cline{1-1} \cline{2-6}  & Stacking LR & CBOW & 92.12 & 92.16 & 92.12 & 92.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance results of models for the Main-AHS dataset. We noticed that the classification performance of the proposed model with CBOW improved accuracy by 0.74%, precision by 0.85%, recall by 0.74%, and f1-score by 0.87% compared to CNN-LSTM with CBOW. Additionally, Figure 5 presents ROC curve and AUC scores of ML, DL models, and the proposed model for the Main-AHS dataset. The proposed model with CBOW word embedding achieved the highest AUC scores at 91. ML models with BI-gram have the lowest AUC scores at 50, 67.421, 69.881, 67.302 for KNN, DT, LR, and RF, respectively, compared to DL models and the proposed models. The proposed model registers the second-best AUC score with SkipGram word embedding at 90. Figure 5: The ROC curve and AUC scores for the Main-AHS dataset.