## 4 Experiment and Result
### Evaluation Baseline
#### 4.4.1 Machine Learning Classifiers  
The results of the cross-validation and testing performance of different ML classifiers on our augmented dataset are illustrated in Table 4-10. In cross-validation, the LR and MNB model achieved the best accuracy of 86.44% and 86.16% for the Word2 Vec feature extraction method. In the LR model, Character 5-gram (C5 Gram) feature has the best precision (86.09%), and the best recall is 96.42% for character 3-gram (C3 Gram). In the MNB classifier, C5 Gram has the best precision (95.79%), while Unigram has the best recall (96.65%). The best F1-Score for both classifiers, LR and MNB, are 89.94% and 87.89% for the combination of Unigram (U), Bigram (B) and Trigram (T) (UBT) with the IF-IDF feature extraction model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Cross-Validation Performance**} & \multicolumn{4}{c}{**Test Performance**} \\ \hline
**Features** & **A (\%)** & **P (\%)** & **R (\%)** & **F1-S (\%)** & **A (\%)** & **P (\%)** & **R (\%)** & **F1-S (\%)** \\ \hline Unigram & 85.27 & 81.63 & 94.31 & 87.52 & 84.10 & 79.11 & 94.58 & 86.16 \\ \hline Bigram & 84.16 & 81.10 & 92.19 & 86.29 & 77.82 & 72.42 & 93.45 & 81.60 \\ \hline Trigram & 82.27 & 80. Table 4-10 display the results of the cross-validation and performance testing of different baseline models on the benchmark dataset. The LR model achieved the best accuracy of 86.44% for the Word2 Vec feature extraction method, character 5-gram (C5 Gram) feature has the best precision of 86.09%, the best recall is 96.42% for character 3-gram (C3 Gram), and the best F1-Score is 89.94% for the combination of Unigram (U), Bigram (B), and Trigram (T) (UBT) with the IF-IDF feature extraction model in cross-validation performance. Tables 4-10 display the results of the cross-validation and performance testing of different baseline models on the benchmark dataset. The LR model achieved the best accuracy of 86.44% for the Word2 Vec feature extraction method, character 5-gram (C5 Gram) feature has the best precision of 86.09%, the best recall is 96.42% for character 3-gram (C3 Gram), and the best F1-Score is 89.94% for the combination of Unigram (U), Bigram (B), and Trigram (T) (UBT) with the IF-IDF feature extraction model in cross-validation performance. \begin{table}
\begin{tabular}{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 & 82.38 & 78.50 & 93.40 & 85.30 & 79.77 & 74.81 & 92.45 & 82.70 \\ \hline Bigram & 81.05 & 77.38 & 91.78 & 83.97 & 75.93 & 70.36 & 93.77 & 80.40 \\ \hline Trigram & 84.88 & 83.21 & 91.20 & 87.02 & 74.54 & 70.14 & 90.45 & 79.01 \\ \hline UBT & 87.88 & 85.26 & 93.30 & 89.10 & 80.77 & 77.89 & 87.62 & 82.47 \\ \hline C3 Gram & 86.66 & 83.38 & 93.38 & 88.10 & 77.93 & 73.40 & 89.51 & 80.66 \\ \hline C4 Gram & 91.11 & 88.09 & 96.85 & 92.26 & 80.38 & 74.02 & 96.28 & 83.70 \\ \hline C5 Gram & 81.05 & 77.38 & 91.78 & 83.97 & 78.27 & 72.64 & 94.19 & 82.02 \\ \hline Fasttext & 85.27 & 83.68 & 91.30 & 87.33 & 80.04 & 74.38 & 95.07 & 83.46 \\ \hline Word2 Vec & 90.27 & 88.09 & 94.45 & 91.16 & 77.15 & 71.19 & 93.65 & 80.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of Decision Tree (DT) machine learning classifier.