Table 12.

Results of machine learning models with data split before applying SMOTE.

Model	Accuracy	Precision	Recall	F1 score
TF-IDF
RF	0.71	0.71	0.71	0.71
AB	0.67	0.69	0.67	0.68
ET	0.72	0.72	0.72	0.72
LR	0.74	0.74	0.74	0.74
MLP	0.70	0.69	0.70	0.70
GBM	0.71	0.72	0.71	0.71
KNN	0.52	0.70	0.52	0.42
ER-VC	0.75	0.75	0.75	0.75
GloVe
RF	0.61	0.61	0.61	0.61
AB	0.54	0.55	0.54	0.54
ET	0.58	0.57	0.58	0.58
LR	0.60	0.60	0.60	0.59
MLP	0.62	0.62	0.62	0.62
GBM	0.54	0.54	0.54	0.54
KNN	0.55	0.57	0.55	0.55
ER-VC	0.63	0.62	0.63	0.62
BoW
RF	0.69	0.71	0.69	0.70
AB	0.67	0.69	0.67	0.68
ET	0.58	0.57	0.58	0.58
LR	0.73	0.73	0.73	0.73
MLP	0.62	0.62	0.62	0.62
GBM	0.72	0.73	0.72	0.72
KNN	0.55	0.57	0.55	0.55
ER-VC	0.73	0.74	0.73	0.74

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