## 4 Experiments Results
### Results
#### 4.4.3 The Performance Results of Models for ASTD Dataset  
This section presents the performance results of the three approaches applied to the ASTD dataset. The first approach is ML models; RF, DT, LR, and KNN were applied with TF-IDF and Unigram, bi-gram. The second approach is the DL models, where DeepCNN, CNN-LSTM, and CNN-GRU were applied with SkipGram and CBOW word embedding. The third approach is the proposed model. Table 6 shows the values of four metrics, including accuracy, precision, recall, and f1-score of testing results for the three approaches. For ML models, LR with unigram had the highest performance (74.79% for accuracy, 72.65% for precision, 74.79% for recall, 70.35 % for f1-score), while DT with Unigram had the lowest (56.61% for accuracy, 63.34% for precision, 56.61% for recall, 58.69% for f1-score). NB with unigram registered the second-highest result (73.55% for accuracy, 70.86% for precision, 73.55% for recall, 69.86% for f1-score). DL models showed an improvement in accuracy, precision, recall, and f1-score compared to ML algorithms. CNN-LSTM with SkipGram achieved the highest performance (78.51% for accuracy, 79.75% for precision, 78.51% for recall, 78.97% for f1-score), and it improved accuracy by 3.72%, precision by 7.1%, recall by 3.72%, and f1-score by 8.62% compared to LR with unigram. DeepCNN with SkipGram reached the lowest performance (71.28% for accuracy, 74.73% for precision, 71.28% for recall, 72.36% for f1-score). We noticed that the classification performance of the proposed model improved accuracy by 2.89%, precision by 0.94%, recall by 2.89%, and f1-score by 1.19% compared to CNN-LSTM with SkipGram. Overall, we can see that the proposed model achieved the highest result compared to ML models and DL models. Additionally, Figure 7 presents ROC curve and AUC scores of ML, DL models, and the proposed model for the ASTD dataset. The proposed model with CBOW word embedding \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{4}{c}{**Testing Performance**} \\ \cline{3-6}  & & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{ML approach} & \multirow{2}{*}{KNN} & Unigram & 71.9 & 51.7 & 71.9 & 60.15 \\ \cline{3-6}  & & Bi-gram & 71.9 & 51.7 & 71.9 & 60.15 \\ \cline{2-6}  & & Unigram & 56.61 & 63.34 & 56.61 & 58.69 \\ \cline{2-6}  & & Bi-gram & 71.9 & 66.43 & 71.9 & 63.46 \\ \cline{2-6}  & & Unigram & 74.79 & 72.65 & 74.79 & 70.35 \\ \cline{2-6}  & & Bi-gram & 71.69 & 65.71 & 71.69 & 63.33 \\ \cline{2-6}  & & Unigram & 73.55 & 70.86 & 73.55 & 69.86 \\ \cline{2-6}  & & Bi-gram & 72.31 & 68.07 & 72.31 & 63.71 \\ \cline{2-6}  & & Unigram & 64.67 & 64.91 & 64.67 & 64.79 \\ \cline{2-6}  & & Bi-gram & 71.9 & 66.36 & 71.9 & 63.17 \\ \hline \multirow{8}{*}{DL approach} & Deep CNN & \multirow{8}{*}{SkipGram} & 71.28 & 74.73 & 71.28 & 72.36 \\ \cline{2-6}  & CNN-LSTM & & 78.51 & 79.75 & 78.51 & 78.97 \\ \cline{1-1} \cline{2-6}  & CNN-GRU & & 77.69 & 79.5 & 77.69 & 78.29 \\ \cline{1-1} \cline{2-6}  & Deep CNN & \multirow{8}{*}{CBOW} & 76.86 & 78.71 & 76.86 & 77.49 \\ \cline{1-1} \cline{2-6}  & CNN-LSTM & & 78.1 & 78.41 & 78.1 & 78.24 \\ \cline{1-1} \cline{2-6}  & CNN-GRU & & 78.1 & 79.35 & 78.1 & 78.56 \\ \hline \multirow{2}{*}{The proposed ensemble model} & Stacking SVM & SkipGram & 80.17 & 79.52 & 80.17 & 79.71 \\ \cline{1-1} \cline{2-6}  & Stacking SVM & CBOW & 81.4 & 80.69 & 81.4 & 80.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance results of models for the ASTD dataset. achieved the highest AUC scores at 77. ML models with Bi-gram have the lowest AUC scores of 50, 52,240, 52.096, 52.527 and 52.383 for KNN, DT, LR, NB, and RF, respectively, compared to DL models and the proposed models. The proposed model registers the best-second AUC score with SkipGram word embedding of 75.