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.
CITATION STYLE
Yousefpour, A., Ibrahim, R., Hamed, H. N. A., & Yokoi, T. (2016). Integrated feature selection methods using metaheuristic algorithms for sentiment analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9621, pp. 129–140). Springer Verlag. https://doi.org/10.1007/978-3-662-49381-6_13
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