3. A computer-implemented method for classifying documents in a collection of documents according to their intended readerships, the method comprising: executing program instructions on a computer to select a document in the collection of documents; executing program instructions on the computer to determine an intended readership of the selected document, the readership being: search engine readership; customer readership; or peer readership; and executing program instructions on the computer to classify the selected document as search engine readership, customer readership, or peer readership according to its determined characteristic; and executing program instructions on the computer to repeat the steps of select a document, determine an intended readership of the selected document, and classify the selected document for additional documents in the collection, wherein the determining an intended readership of the document is at least partially based on using at least one of: number and type of stop words contained in the document; and number and type of word associations contained in the document; wherein at least some documents of the collection are classified as search engine readership, at least some documents are classified as customer readership, and at least some documents are classified as peer readership; wherein determining an intended readership of the document comprises: a training phase, wherein the training phase generates a set of word association patterns from the document based on a set of labeled training texts; and a testing phase, wherein the testing phase applies the word association patterns to classify the document; and wherein the training phase comprises: preparing a list that includes all stop words; for a set of labeled training texts having a common class (C i ), counting the number of occurrences of at least one word association pattern within a distance (D) in the document; selecting the at least one word association pattern if the distance (D) is greater than a support threshold (S); and outputting the at least one selected word association pattern for the document.
4. The method of claim 3 , wherein the testing phase further comprises: classifying the document based on a class having a highest probability value (Q i ) for each word association pattern (p) in the document, wherein Q i = ∑ p ∈ P ⁢ E ⁡ ( p , C i ) , where P is the set of all word association patterns p and where E(p, C i )=O(p, C i )/ΣO(p, C i ), and O(p, C i ) is the number of occurrences of the pattern p in class C i .