Using Wavelets to Classify Documents

Currently, Fourier and cosine discrete transformations are used to classify documents. This article proposes a new strategy that uses wavelets in the representation and reduction of data text. Wavelets have been extensively used for dimensionality reduction in the field of signal processing. In this work, we show that a text document, after being subjected to a simple process of reorganization of its terms, can be treated like a signal and analyzed by signal processing tools. We demonstrate that this new representation is able to describe the most relevant features of documents in a synthetic representation and this new perspective improves the performance of the classification algorithm.