## 4 Experiment and Result
### Evaluation Baseline
#### 4.4.1 Machine Learning Classifiers
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Cross-Validation Performance**} & \multicolumn{4}{c}{**Test Performance**} \\ \hline
**Features** & **A (\%)** & **P (\%)** & **R (\%)** & **F1-S (\%)** & **A (\%)** & **P (\%)** & **R (\%)** & **F1-S (\%)** \\ \hline Bigram & 87.27 & 87.43 & 89.64 & 88.52 & 81.16 & 77.22 & 90.75 & 83.44 \\ \hline Bigram & 87.72 & 92.53 & 84.07 & 88.10 & 80.71 & 91.67 & 69.69 & 79.18 \\ \hline Trigram & 85.05 & 87.11 & 85.80 & 86.45 & 78.88 & 73.31 & 94.54 & 82.58 \\ \hline UBT & 87.33 & 83.14 & 95.50 & 88.89 & 83.44 & 78.60 & 93.33 & 85.33 \\ \hline C3 Gram & 88.44 & 84.88 & 95.06 & 89.68 & 81.55 & 76.54 & 92.43 & 83.74 \\ \hline C4 Gram & 92.22 & 92.63 & 93.20 & 92.91 & 82.21 & 76.07 & 96.28 & 84.99 \\ \hline C5 Gram & 87.72 & 92.53 & 84.07 & 88.10 & 81.71 & 93.40 & 70.22 & 80.17 \\ \hline Fasttext & 87.49 & 88.87 & 88.60 & 88.73 & 79.60 & 73.07 & 97.38 & 83.49 \\ \hline Word2 Vec & 90.66 & 87.44 & 96.23 & 91.63 & 78.60 & 71.90 & 96.12 & 82.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of Random Forest (RF) machine learning classifier. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Cross-Validation Performance**} & \multicolumn{4}{c}{**Test Performance**} \\ \hline
**Features** & **A (\%)** & **P (\%)** & **R (\%)** & **F1-S (\%)** & **A (\%)** & **P (\%)** & **R (\%)** & **F1-S (\%)** \\ \hline Unigram & 82.99 & 79.04 & 93.81 & 85.79 & 78.60 & 72.68 & 94.69 & 82.23 \\ \hline Bigram & 83.60 & 79.89 & 93.11 & 86.00 & 77.10 & 71.52 & 93.88 & 81.19 \\ \hline Trigram & 82.10 & 79.79 & 90.80 & 84.94 & 77.10 & 71.55 & 94.23 & 81.34 \\ \hline UBT & 88.49 & 84.44 & 96.02 & 89.86 & 83.05 & 77.96 & 93.65 & 85.09 \\ \hline C3 Gram & 86.27 & 87.05 & 86.96 & 87.01 & 83.38 & 81.74 & 87.14 & 84.35 \\ \hline C4 Gram & 81.21 & 81.75 & 84.57 & 83.13 & 62.53 & 58.70 & 95.75 & 72.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of Stochastic Gradient Descent (SGD) machine learning classifier. In terms of test performance, the LR model achieved the best accuracy, precision, and f1-score with UBT and the best recall with Word2 Vec (accuracy = 86.21%, precision = 81.62%, recall = 96.59%, f1-score = 87.64%). The MNB model achieved the best accuracy of 82.05% with Word2 Vec, best precision of 80.39 with C3 Gram, best recall of 99.56 with C5 Gram, and best f1-score of 85.11% with Word2 Vec on the test data. In cross validation, the DT model achieved the highest performance with C4 Gram (accuracy = 91.11%, precision = 88.09%, recall = 96.85% and f1-score = 92.26%), whereas the lowest performance was achieved with Bigram. The RF model achieved the best accuracy, precision, and f1-score with C4 Gram (92.22%, 92.63%,  
best precision and recall with C5 Gram and Fasttext (accuracy = 83.44%, precision = 93.40%, recall = 97.38, f1-score = 85.33%). In cross-validation, the SGD model achieved the highest accuracy and recall with UBT, highest precision with C3 Gram, highest f1-score C4 Gram (accuracy = 88.49%, precision = 87.05%, recall = 96.02% and f1-score = 89.86%). The SGD model achieved high accuracy and precision with C3 Gram, high recall with Word2 Vec, and a high f1-score with UBT (accuracy = 83.38%, precision = 81.74%, recall = 99.43% and f1-score = 85.09%). The SVM model achieved the best accuracy, precision, and f1-score with Word2 Vec and the highest recall with C3 Gram (accuracy = 90.99%, precision = 87.87%, recall = 97.16%, f1-score = 91.91%) in cross-validation. In test performance, the SVM model achieved the best accuracy, precision, and f1-score with Unigram and the best recall with Word2 Vec (accuracy = 84.94%, precision = 80.23%, recall = 94.47% and f1-score = 86.77%). The KNN model obtained the best accuracy and precision with C3 Gram, and the best recall and f1-score with Word2 Vec in cross-validation (accuracy = 87.44%, precision = 87.56%, recall = 94.97% and f1-score = 88.40%).