한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제24권2호
  • /
  • Pages.171-177
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  • 2019
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Exploring an Optimal Feature Selection Method for Effective Opinion Mining Tasks

  • Eo, Kyun Sun (SKKU Business School, Sungkyunkwan University) ;
  • Lee, Kun Chang (SKKU Business School/SAIHST (Samsung Advanced Institute of Health Sciences & Technology), Sungkyunkwan University)
  • 투고 : 2018.11.13
  • 심사 : 2019.01.07
  • 발행 : 2019.02.28
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초록

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.

키워드

Table 1. Study of Opinion mining

CPTSCQ_2019_v24n2_171_t0001.png 이미지

Table 2. Results of Accuracy

CPTSCQ_2019_v24n2_171_t0002.png 이미지

Table 4. Results of AUC

CPTSCQ_2019_v24n2_171_t0003.png 이미지

Table 3. The number of features

CPTSCQ_2019_v24n2_171_t0004.png 이미지

Table 5. Results of T-test

CPTSCQ_2019_v24n2_171_t0005.png 이미지

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