Design of Ensemble Classifier Selection Framework Based on Ant Colony Optimization for Sentiment Analysis and Opinion Mining
Sanjeev Kumar1, Ravendra Singh2

1Sanjeev Kumar*, Department of CS & IT, M.J.P. Rohilkhand University, Bareilly, India.
2Dr. Ravendra Singh, Department of CS & IT, M.J.P. Rohilkhand University, Bareilly, India.
Manuscript received on September 19, 2019. | Revised Manuscript received on 27 September, 2019. | Manuscript published on October 10, 2019. | PP: 5152-5117 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27551081219/2019©BEIESP | DOI: 10.35940/ijitee.L2755.1081219
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Abstract: Ensemble Classifier provides a promising way to improve the accuracy of classification for sentiment analysis and opinion mining. Ensemble classifier should combine with diverse base classifiers. However, establishing a connection between diversity and accuracy of ensemble classifier is tedious task because of sensitivity between diversity and accuracy. In this paper an Ensemble classifier selection (ECS) framework based on Ant Colony Optimization (ACO) algorithm is presented. The framework provides a subset of base classifiers from a given set of classifiers with maximum possible diversity and accuracy to design an ensemble classifier for sentiment analysis and opinion mining. This framework uses diversity measures and accuracy as selection criteria for classifier selection for ensemble creation. The experimental result shows that the ensemble classifiers provided by this framework presents an efficient way for sentiment analysis and opinion mining.
Keywords:  Ant Colony Optimization, Ensemble Classifier, Sentiment Analysis and Opinion Mining, Ensemble Classifier Selection
Scope of the Article: Discrete Optimization