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http://dx.doi.org/10.14400/JDC.2019.17.2.163

Investigating Opinion Mining Performance by Combining Feature Selection Methods with Word Embedding and BOW (Bag-of-Words)  

Eo, Kyun Sun (SKK Business School, Sungkyunkwan University)
Lee, Kun Chang (Global Business Administration/Dept of Health Sciences & & Technology, SHAIHST Sungkyunkwan University)
Publication Information
Journal of Digital Convergence / v.17, no.2, 2019 , pp. 163-170 More about this Journal
Abstract
Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.
Keywords
Word embedding; Opinion mining; Sentiment analysis; Feature selection; Machine learning;
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