5. The method of claim 4 , further comprising training a particular model for the particular class by: providing a training set of messages that belong to the particular class and messages that do not belong to the particular class; determining a plurality of feature values for each message in the training set; and continuing to adjust the particular model as a function of the features values of each message in the training set and the particular lexicon until the particular model classifies the training set with a predetermined level of accuracy.
5. The method of claim 4 , further comprising training a particular model for the particular class by: providing a training set of messages that belong to the particular class and messages that do not belong to the particular class; determining a plurality of feature values for each message in the training set; and continuing to adjust the particular model as a function of the features values of each message in the training set and the particular lexicon until the particular model classifies the training set with a predetermined level of accuracy. 6. The method of claim 5 , wherein the feature values correspond to the percentage of message tokens that are in the particular lexicon and one or more of the following: percentage of numeric message tokens, percentage of message tokens from a junk lexicon, percentage of message tokens that are URL's (universal resource locators), message length, and percentage of message tokens that are pronouns.