Table 5.

Classification results of machine learning models using TF-IDF without SMOTE.

Models	Accuracy	Precision	Recall	F1 score
RF	0.71	0.70	0.71	0.70
AB	0.64	0.65	0.64	0.64
ET	0.71	0.70	0.71	0.70
LR	0.73	0.73	0.73	0.72
MLP	0.71	0.71	0.71	0.71
GBM	0.70	0.70	0.70	0.70
kNN	0.66	0.65	0.66	0.65
ER-VC	0.72	0.72	0.72	0.71

RF: Random Forest; LR: Logistic Regression; MLP: Multilayer Perceptron; GBM: Gradient Boosting Machine; AB: AdaBoost, kNN: k Nearest Neighbours; ET: Extra Tree Classifier; TF-IDF: Term Frequency-Inverse Document Frequency; SMOTE: Synthetic Minority Oversampling Approach; ER-VC: Extreme Regression-Voting Classifier.