Table 2:

K-fold cross validation of different machine learning classifiers: MLPClassifier (MLP), XGBClassifier (XGB), KNeigh-borsClassifier (KNN), RandomForestClassifier (RF), DecisionTreeClassifier (DT) using two different types of features (1) tf-idf and word2vec trained on twitter and (2) tf-idf and word2vec trained Wikipedia, PubMed and PMC. On the basis of reported precision or positive predictive value (PPV), recall or true positive rate (TPR) and F1 score (F1), MLPClassifier turned out to be the best performing classifier using tf-idf and word2vec trained on twitter.

	Using tf-idf and word2vec trained on twitter									Using tf-idf and word2vec trained Wikipedia, PubMed and PMC
		3-fold			5-fold			10-fold			3-fold			5-fold			10-fold
		PPV	Recall	F1	PPV	TPR	F1	PPV	TPR	F1	PPV	TPR	F1	PPV	TPR	F1	PPV	TPR	F1
MLP	0.62	0.71	0.66	0.76	0.75	0.76	0.80	0.82	0.80	0.63	0.71	0.66	0.74	0.75	0.75	0.79	0.81	0.79
XGB	0.58	0.68	0.62	0.71	0.73	0.72	0.78	0.81	0.79	0.58	0.70	0.63	0.72	0.73	0.72	0.78	0.79	0.78
KNN	0.59	0.55	0.57	0.67	0.59	0.62	0.70	0.62	0.66	0.59	0.66	0.62	0.65	0.68	0.66	0.69	0.71	0.70
RF	0.62	0.63	0.62	0.72	0.64	0.66	0.74	0.67	0.70	0.61	0.62.	0.63	0.72	0.63	0.67	0.74	0.68	0.69
DT	0.54	0.64	0.59	0.64	0.68	0.65	0.69	0.70	0.69	0.56	0.64	0.60	0.63	0.66	0.65	0.67	0.71	0.68