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@article{ART002649850},
author={Kyun Sun Eo and Kun-Chang Lee},
title={Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2020},
volume={25},
number={11},
pages={41-50},
doi={10.9708/jksci.2020.25.11.041}

TY - JOUR
AU - Kyun Sun Eo
AU - Kun-Chang Lee
TI - Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms
JO - Journal of The Korea Society of Computer and Information
PY - 2020
VL - 25
IS - 11
PB - The Korean Society Of Computer And Information
SP - 41
EP - 50
SN - 1598-849X
AB - 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.
KW - Sentiment analysis;Feature selection;Particle Swarm Optimization;Multi Objective Evolutionary Algorithm;Machine learning
DO - 10.9708/jksci.2020.25.11.041
ER -

Kyun Sun Eo and Kun-Chang Lee. (2020). Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms. Journal of The Korea Society of Computer and Information, 25(11), 41-50.

Kyun Sun Eo and Kun-Chang Lee. 2020, "Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms", Journal of The Korea Society of Computer and Information, vol.25, no.11 pp.41-50. Available from: doi:10.9708/jksci.2020.25.11.041

Kyun Sun Eo, Kun-Chang Lee "Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms" Journal of The Korea Society of Computer and Information 25.11 pp.41-50 (2020) : 41.

Kyun Sun Eo, Kun-Chang Lee. Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms. 2020; 25(11), 41-50. Available from: doi:10.9708/jksci.2020.25.11.041

Kyun Sun Eo and Kun-Chang Lee. "Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms" Journal of The Korea Society of Computer and Information 25, no.11 (2020) : 41-50.doi: 10.9708/jksci.2020.25.11.041

Kyun Sun Eo; Kun-Chang Lee. Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms. Journal of The Korea Society of Computer and Information, 25(11), 41-50. doi: 10.9708/jksci.2020.25.11.041

Kyun Sun Eo; Kun-Chang Lee. Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms. Journal of The Korea Society of Computer and Information. 2020; 25(11) 41-50. doi: 10.9708/jksci.2020.25.11.041

Kyun Sun Eo, Kun-Chang Lee. Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms. 2020; 25(11), 41-50. Available from: doi:10.9708/jksci.2020.25.11.041

Kyun Sun Eo and Kun-Chang Lee. "Exploring the Feature Selection Method for Effective Opinion Mining: Emphasis on Particle Swarm Optimization Algorithms" Journal of The Korea Society of Computer and Information 25, no.11 (2020) : 41-50.doi: 10.9708/jksci.2020.25.11.041