A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams
Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification.
['[EMPTY]', '[ITALIC] F1', '[ITALIC] π', '[ITALIC] ρ']
It is interesting to notice that we also performed classification of subjects on the test set using all subject’s writings as if it were a single document (i.e. classical timeless classification); results are shown in SS3 obtained the highest values for F1 (0.61) and Precision (0.63) measures, possibly due to the flexibility that is given by its three hyper-parameters to discover important and discriminative terms. These results provide strong evidence that SS3 also achieves competitive performance when is trained and tested to optimize standard (non-temporal) evaluation measures. Note that the best configuration of MNB obtained after the model selection stage, aiming at overcoming the unbalanced dataset problem, tends to classify all subjects as depressed, that is the reason MNB had a Recall(ρ) close to 1 but a really poor precision (0.25).