There are lots of applications of text classification in the commercial world; email spam filtering is perhaps now the most ubiquitous. Jackson and Moulinier (2002) write: ``There is no question concerning the commercial value of being able to classify documents automatically by content. There are myriad potential applications of such a capability for corporate Intranets, government departments, and Internet publishers.''
Most of our discussion of classification has focused on introducing various machine learning methods rather than discussing particular features of text documents relevant to classification. This bias is appropriate for a textbook, but is misplaced for an application developer. It is frequently the case that greater performance gains can be achieved from exploiting domain-specific text features than from changing from one machine learning method to another. Jackson and Moulinier (2002) suggest that ``Understanding the data is one of the keys to successful categorization, yet this is an area in which most categorization tool vendors are extremely weak. Many of the `one size fits all' tools on the market have not been tested on a wide range of content types.'' In this section we wish to step back a little and consider the applications of text classification, the space of possible solutions, and the utility of application-specific heuristics.