Ontology based Feature Selection and Weighting for Text classification using Machine Learning

Abstract

Text classification consists in attributing text (document) to its corresponding class (category). It can be performed using an artificial intelligence technique called machine learning. However, before training the machine learning model that classifies texts, three main steps are also mandatory: (1) Preprocessing, which cleans the text; (2) Feature selection, which chooses the features that significantly represent the text; and (3) Feature weighting, which aims at numerically representing text through feature vector. In this paper, we propose two algorithms for feature selection and feature weighting. Unlike most existing works, our algorithms are sense-based since they use ontology to represent, not the syntax, but the sense of a text as a feature vector. Experiments show that our approach gives encouraging results compared to existing works. However, some additional suggested improvements can make these results more impressive.