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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)
  • Received : 2018.11.13
  • Accepted : 2019.01.07
  • Published : 2019.02.28

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

Table 1. Study of Opinion mining

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Table 2. Results of Accuracy

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Table 4. Results of AUC

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Table 3. The number of features

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Table 5. Results of T-test

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