Automatic text categorization: case study

Text categorization is a process of classifying documents with regard to a group of one or more existent categories according to themes or concepts present in their contents. The most common application of it is in information retrieval systems (IRS) to document indexing. A method to transform text categorization into a viable task is to use machine-learning algorithms to automate text classification, allowing it to be carried out fast, into concise manner and in broad range. The objective of this work is to present and compare the results of experiments on text categorization using artificial neural networks of multilayer perceptron and self-organizing map types, and traditional machine-learning algorithms used in this task: C4.5 decision tree, PART decision rules and Naive Bayes classifier.