Table 1. Overview table of AUC values of various feature extraction methods used to train different ML algorithms and evaluated with 10-fold cross-validation on the training dataset. : BOW: bag of words; LDA: Latent Dirichlet allocation; LR: Logistic regression; NMF: Non-negative matrix factorization; NN: Neural network; PCA: Principal component analysis; SVM: Support vector machine; TF-IDF: Term frequency-inverse document frequency.

	NN	SVM	LR	Average AUC
Dummy	0.5
BOW	0.99	0.97	0.97	0.977
TF-IDF	0.99	0.96	0.96	0.970
NMF	0.98	0.9	0.9	0.927
PCA	0.95	0.91	0.9	0.920
LDA	0.94	0.89	0.88	0.903
Doc2Vec	0.94	0.9	0.85	0.897