1. A method of feature ideation, comprising: determining a plurality of errors in a training set of labeled textual data; determining a set of candidate features to correct at least one error of the plurality of errors; receiving a selection of at least one candidate feature of the set of candidate features to be an applied feature; and retraining a classifier based on the applied feature.