In order to reduce the high dimensional feature space in the text classification, feature selection plays a significant role. The dimension reduction of feature space reduces the computation cost and improves the text classification system accuracy. Hence, the identification of an optimal combination of features is an essential task in text classification. In this paper, the proposed work introduces a novel hybrid feature selection method based on binary poor and rich optimization algorithm (HBPRO) to obtain the appropriate subset of optimal features. The optimal feature subset which is selected by our proposed work is evaluated using Nave Bayes classifier with two popular benchmark text corpus datasets. The experimental results confirm that the proposed feature selection scheme (HBPRO) produces higher accuracy with a reduced number of features when compared with other feature selection techniques.