Rapid Classification of Crisis-Related Data on Social Networks usingConvolutional Neural Networks
TABLE III: (left table) The AUC scores of non-neural and neural network-based classifiers. event, out and event+out represents the three different settings of the training data – event only, out-of-event only and a concatenation of both. (right table) Confusion matrix for binary classification of the Nepal EQ event. The rows show the actual class as in the gold standard and the column shows the number of tweets predicted in that class.
['Event', 'SVM Event', 'SVM Event', 'CNN [ITALIC] I Event', 'CNN [ITALIC] I Event']
CNNs performed better than all non-neural classifiers for all events under consideration. The SVM prediction is inclined to favor the Informative class whereas CNN predicted more instances as non-informative than informative. In the case of out-of-event training, SVM predicted most of the instances as informative. Thus, it achieved high recall, but very low precision. CNN, on the other hand, achieved quite balanced precision and recall numbers.