Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm

Abstract

Abstract Artificial bee colony (ABC) optimization and imperialist competitive algorithm (ICA) are two famous metaheuristic methods. In ABC, exploration is good because each bee moves toward random neighbors in the first and second phases. In ABC, exploitation is poor because it does not try to examine a promising region of search space carefully to see if it contains a good local minimum. In this study, ICA is considered to improve ABC exploitation, and two novel swarm-based hybrid methods called ABC–ICA and ABC–ICA1 are proposed, which combine the characteristics of ABC and ICA. The proposed methods improve the evaluations results in both continuous and discrete environments compared to the baseline methods. The second method improves the first optimization method as well. Feature selection can be considered to be an optimization problem because selecting the appropriate feature subset is very important and the action of appropriate feature selection has a great influence on the efficiency of classifier algorithms in supervised methods. Therefore, to focus on feature selection is a key issue and is very important. In this study, different discrete versions of the proposed methods have been introduced that can be used in feature selection and feature scoring problems, which have been successful in evaluations. In this study, a problem called cold start is introduced, and a solution is presented that has a great impact on the efficiency of the proposed methods in feature scoring problem. A total of 16 UCI data sets and 2 Amazon data sets have been used for the evaluation of the proposed methods in feature selection problem. The parameters that have been compared are classification accuracy and the number of features required for classification. Also, the proposed methods can be used to create a proper sentiment dictionary. Evaluation results confirm the better performance of the proposed methods in most experiments.