This paper describes a text-document-filtering software tool TEA (TExt Analyzer), which was originally developed for physicians to support selections of large numbers of unstructured medical text documents obtained from available Internet services. TEA learns interesting and relevant documents for individual users basically by the naive Bayes algorithm. Moreover, TEA provides a number of additional functions that improve its classification accuracy. The learning process of TEA is based on a set of labeled positive and negative examples of text documents, which obtain their labels from users interested in documents of certain, usually very specific topics. Experiments and real uses of TEA by physicians have demonstrated that a classification accuracy---separating the documents between two classes (interesting and uninteresting)---can be expected from 70% up to 97%, typically 85% and better.