With the exponential increase of available data amount on the web, especially text, the need for algorithms capable of automatically extracting information from these resources is even more clear.
In this paper I introduce a survey of the theoretical basis of several Machine Learning algorithms and I also produce automatic categorization experiments of forum posts, using a public dataset. The results are presented and comparatively and qualitatively discussed.
The results shows that a Decision Tree algorithm had the best performance with 97.67% of accuracy while another Neural Network algorithm had only 12.33%. Other algorithms are presented in order to compare how each other adaptable they are in this scenario, considering that these algorithms were not developed speci cally to this case and were already built.
Keywords: Arti cial Intelligence, Text Mining, Machine Learning, Decision Trees, Neural Networks, Bayesian Learning, clustering, Support Vector Machines.