You need to create an active learning loop over the process of the learning. Try to start from a history of tickets and using doc2vec to get the similarity. When you find a bad result in the result of your classifier, then report it and then try to retrain the classifier. Also, you can wait to retrain the model, up to finding the predefined batch0size of the new data which are not in the training set. 

Also, to get a better result in the active learning loop, you can testify incoming data by the measuring of the classifier uncertainty over it. If the entropy of the classifier over the data is not in a good situation, you can label the data by the operator (as an oracle) and then up reach to the predefined batch-size, retrain the classifier.

Morevoer, to know better about the active learning process and query strategies follow [this link][1].


  [1]: https://en.wikipedia.org/wiki/Active_learning_(machine_learning)