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http://dx.doi.org/10.9708/jksci.2020.25.11.041

Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms  

Eo, Kyun Sun (SKK Business School, Sungkyunkwan University)
Lee, Kun Chang (SKK Business School/SAIHST (Samsung Advanced Institute of Health Sciences & Technology), Sungkyunkwan University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.11, 2020 , pp. 41-50 More about this Journal
Abstract
Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.
Keywords
Sentiment analysis; Feature selection; Particle Swarm Optimization; Multi Objective Evolutionary Algorithm; Machine learning;
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