Classification is concerned with taking a specific document and determining if it fits into one of several other document groups. There are two basic techniques for classifying text:

Rule-based classification uses a combination of words and other attributes organized around expert crafted rules. These can be very effective but creating them is a time-consuming process.

Supervised Machine Learning (SML) takes a collection of annotated training documents to create a model. The model is normally called the classifier. There are many different machine learning techniques including Naive Bayes, Support-Vector Machine (SVM), and k-nearest neighbor.

We are not concerned with how these approaches work but the interested reader will find innumerable sources that expand upon these and other techniques.