Integrated feature selection methods using metaheuristic algorithms for sentiment analysis

Abstract

In text mining, the feature selection process can potentially improve classification accuracy by reducing the high-dimensional feature space to a low-dimensional feature space resulting in an optimal subset of available features. In this paper, a hybrid method and two meta-heuristic algorithms are employed to find an optimal feature subset. The feature selection task is performed in two steps: first, different feature subsets (called local-solutions) are obtained using a hybrid filter and wrapper approaches to reduce high-dimensional feature space; second, local-solutions are integrated using two meta-heuristic algorithms (namely, the harmony search algorithm and the genetic algorithm) in order to find an optimal feature subset. The results of a wide range of comparative experiments on three widely-used datasets in sentiment analysis show that the proposed method for feature selection outperforms other baseline methods in terms of accuracy.