Table 1.

Performance measures for tobacco relevance, positive sentiment, and negative sentiment classification tasks using 500 features (baseline classification accuracies [majority class] are 57% for relevance, 74% for positive sentiment, and 82% for negative sentiment).

Features		Naïve Bayes					KNN					SVM
		Acca	F	Preb	Recc	Sped	Acc	F	Pre	Rec	Spe	Acc	F	Pre	Rec	Spe
Relevance
	Unigrams	0.77	0.83	0.73	0.95	0.53	0.73	0.78	0.73	0.83	0.59	0.82	0.85	0.82	0.88	0.75
	Bigrams	0.66	0.77	0.63	0.97	0.24	0.65	0.76	0.63	0.97	0.24	0.73	0.75	0.82	0.69	0.79
	Trigrams	0.61	0.74	0.6	0.99	0.1	0.6	0.74	0.59	0.97	0.11	0.61	0.74	0.59	0.99	0.1
Positive sentiment
	Unigrams	0.76	0.5	0.56	0.45	0.87	0.76	0.37	0.58	0.27	0.93	0.75	0.38	0.53	0.3	0.91
	Bigrams	0.77	0.44	0.62	0.34	0.93	0.76	0.42	0.58	0.33	0.92	0.77	0.43	0.61	0.33	0.92
	Trigrams	0.76	0.26	0.62	0.16	0.96	0.76	0.26	0.62	0.17	0.96	0.76	0.27	0.61	0.17	0.96
Negative sentiment
	Unigrams	0.84	0.52	0.57	0.48	0.92	0.72	0.3	0.27	0.33	0.8	0.83	0.39	0.53	0.3	0.94
	Bigrams	0.85	0.35	0.73	0.23	0.98	0.31	0.3	0.18	0.82	0.2	0.84	0.44	0.59	0.35	0.95
	Trigrams	0.84	0.24	0.76	0.14	0.99	0.22	0.3	0.18	0.94	0.07	0.84	0.37	0.66	0.25	0.97

^aAcc: accuracy.

^bPre: precision.

^cRec: recall.

^dSpe: specificity.