3. The method of claim 1 , wherein obtaining the models and classification are performed using a Gradient Boosted Decision Trees algorithm, the method further comprising generating a particular lexicon for a particular class by: manually classifying a sample of messages as belonging to the particular class; forming the particular lexicon from a top percentage of most frequently used tokens from the sample of messages; automatically clustering a larger sample of messages into a plurality of topics; and to the particular lexicon, adding tokens from one or more topics that also include one or more tokens the particular lexicon.
3. The method of claim 1 , wherein obtaining the models and classification are performed using a Gradient Boosted Decision Trees algorithm, the method further comprising generating a particular lexicon for a particular class by: manually classifying a sample of messages as belonging to the particular class; forming the particular lexicon from a top percentage of most frequently used tokens from the sample of messages; automatically clustering a larger sample of messages into a plurality of topics; and to the particular lexicon, adding tokens from one or more topics that also include one or more tokens the particular lexicon. 7. The method of claim 3 , wherein the clustering is performed by a Latent Dirichlet Allocation algorithm.