High dimensional sentiment classification of product reviews using evolutionary computation

Abstract

Feature selection is an important process in text classification. In general, traditional feature selection approaches are based on exhaustive search hence become inefficient due to a large search space. Further, this task becomes more challenging as the number of features increases. Recently, evolutionary computation (EC)-based search techniques have received a lot of attention in solving feature selection problem in high-dimensional feature space. This paper proposes a particle swarm optimisation (PSO)-based feature selection approach which is capable of generating the desired number of high-quality features from a large feature space. The proposed algorithm is tested on a large dataset and compared with several existing state-of-the-art algorithms used for feature selection. The accuracy of the underlying classifier has been considered as a measure of performance. Our obtained results demonstrated that the proposed PSO-based feature selection approach outperforms the other traditional feature selection algorithms in all the considered classifiers.