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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 LEE, KUN CHANG 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.

Citation status

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

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