Universiti Teknologi Malaysia Institutional Repository

Integrated feature selection methods using metaheuristic algorithms for sentiment analysis

Yousefpour, A. and Ibrahim, R. and Hamed, H. N. A. and Yokoi, T. (2016) Integrated feature selection methods using metaheuristic algorithms for sentiment analysis. In: 8th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2016, 14 - 16 March 2016, Vietnam.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Feature selection, Genetic algorithm, Harmony search, Integration feature selection methods, Sentiment analysis
Subjects:Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Divisions:Computing
ID Code:73588
Deposited By: Mohd Zulaihi Zainudin
Deposited On:20 Nov 2017 08:43
Last Modified:20 Nov 2017 08:43

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