1. A method for textual classification, comprising: receiving, by a processing unit, a training set of textual data; classifying, by the processing unit, the training set of textual data to obtain a first plurality of classifications for the training set of textual data; determining, by the processing unit, a plurality of errors based on differences between the first plurality of classifications and a first plurality of labels having been previously assigned to the training set of textual data; determining, by the processing unit, a set of candidate features based on the determined plurality of errors to correct at least one error of the plurality of errors; causing, by the processing unit, a display of one or more candidate features from the determined set of candidate features for selection as an applied feature; receiving, by the processing unit, a selection of at least one candidate feature of the displayed one or more candidate features to be an applied feature; and retraining a classifier, using the applied feature, to re-classify the training set of textual data.