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http://dx.doi.org/10.9708/jksci.2019.24.02.171

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)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.2, 2019 , pp. 171-177 More about this Journal
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;
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