I'm facing a text classification problem, and I need to classify examples to 34 groups.
The problem is, the size of training data of 34 groups are not balanced. For some groups I have 2000+ examples, while for some I only have 100+ examples.
For some small groups, the classification accuracy is quite high. I guess those groups may have specific key words to recognize and classify. While for some, the accuracy is low, and the prediction always goes to large groups.
I want to know how to deal with the "low frequency example problem". Would simply copy and duplicate the small group data work? Or I need to choose the training data and expand and balance the data size? Any suggestions?