Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks (효과적인 오피니언 마이닝을 위한 최적속성선택에 관한 연구)

Kyun Sun Eo (어균선); Kun-Chang Lee (이건창)

doi:10.9708/jksci.2019.24.02.171

Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2019, 24(2), pp.171-177
DOI : 10.9708/jksci.2019.24.02.171
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : November 13, 2018
Accepted : January 7, 2019
Published : February 28, 2019

Kyun Sun Eo ¹, Kun-Chang Lee ¹

¹성균관대학교

Accredited

ABSTRACT

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

KEYWORDS

Opinion mining, Sentiment analysis, Feature selection

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks

ABSTRACT

KEYWORDS

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Citation status

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* References for papers published after 2023 are currently being built.