Text classification models for the automatic detection of nonmedical prescription medication use from social media

Table 3 Performances of transformer- and fusion-based models in terms of class-specific recall, precision and F₁-scores, and overall accuracy

Classification algorithm	Precision				Recall				F₁-score				Accuracy (%)
Classification algorithm	A	C	M	U	A	C	M	U	A	C	M	U	Accuracy (%)
BERT-1	0.60	0.78	0.86	0.88	0.61	0.77	0.85	0.89	0.60	0.77	0.86	0.89	79.48
BERT-2	0.60	0.79	0.86	0.91	0.61	0.77	0.86	0.85	0.61	0.78	0.86	0.88	79.85
RoBERTa	0.63	0.81	0.88	0.90	0.66	0.82	0.87	0.89	0.65	0.81	0.88	0.90	82.32
AlBERT	0.66	0.81	0.88	0.86	0.63	0.83	0.88	0.88	0.65	0.82	0.88	0.87	82.78
XLNet	0.65	0.77	0.86	0.87	0.55	0.83	0.86	0.82	0.60	0.80	0.86	0.85	80.52
DistilBERT	0.56	0.75	0.86	0.89	0.60	0.77	0.83	0.87	0.58	0.76	0.84	0.88	78.0
Proposed Fusion-1	0.60	0.84	0.91	0.78	0.76	0.81	0.84	0.93	0.67	0.82	0.87	0.85	82.22
Proposed Fusion-2	0.67	0.83	0.87	0.88	0.62	0.83	0.90	0.89	0.65	0.83	0.89	0.88	83.43
Proposed Fusion-3	0.56	0.83	0.90	0.75	0.73	0.80	0.83	0.92	0.64	0.82	0.86	0.82	80.92
Proposed Fusion-4	0.68	0.84	0.87	0.89	0.62	0.82	0.90	0.87	0.64	0.83	0.89	0.88	83.49

ISSN: 1472-6947