Supervised text categorisation is a significant tool considering the vast amount of structured, unstruc-tured, or semi-structured texts that are available from internal or external enterprise resources. The goal of supervised text categorisation is to assign text documents to finite pre-specified categories in order to extract and automatically organise information coming from these resources. This paper pro-poses the implementation of a generic application – SVM Categorizer using the Support Vector Ma-chines algorithm with an innovative statistical adjustment that improves its performance. The algo-rithm is able to learn from a pre-categorised document corpus and it is tested on another uncatego-rized one based on a business intelligence case study. This paper discusses the requirements, design and implementation and describes every aspect of the application that will be developed. The final output of the SVM Categorizer is evaluated using commonly accepted metrics so as to measure its per-formance and contrast it with other classification tools
