Table 2. Overview table of accuracy 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 Accuracy
Dummy	0.5
BOW	0.96	0.97	0.97	0.967
TF-IDF	0.95	0.96	0.97	0.96
NMF	0.94	0.91	0.9	0.917
PCA	0.91	0.9	0.9	0.903
LDA	0.88	0.89	0.88	0.883
Doc2Vec	0.87	0.9	0.86	0.877