Syndromic classification of Twitter messages
Table 3: Evaluation of automated syndrome classification using naive Bayes and Support Vector Machine models on 10-fold cross validation. P - Precision, R - Recall, F1 - F1 score. 1 SVM using a linear kernel, 2 SVM using a polynomial kernal degree 2, 3 SVM using a polynomial kernal degree 3, R SVM using a radial basis function kernel.
['Synd.', 'Naive Bayes P', 'Naive Bayes R', 'Naive Bayes F1', 'SVM1 P', 'SVM1 R', 'SVM1 F1', 'SVM2 P', 'SVM2 R', 'SVM2 F1', 'SVM3 P', 'SVM3 R', 'SVM3 F1', 'SVM [ITALIC] R P', 'SVM [ITALIC] R R', 'SVM [ITALIC] R F1']
Overall the SVM with polynomial degree 1 kernel outperformed all other kernels with other kernels generally offering better precision at a higher cost to recall. Precision (Positive predictive) values ranged from 82.0 to 93.8 for SVM (polynomial degree 1) and from 83.3 to 99.0 for NB. Recall (sensitivity) values ranged from 58.3 to 96.2 for SVM (polynomial degree 1) and from 74.7 to 90.3 for NB. SVM tended to offer a reduced level of precision but better recall. In the case of one syndrome (Hemorrhagic) we noticed an unusually low level of recall for SVM but not for NB.