IWD Based Feature Selection Algorithm for Sentiment Analysis

Tuba Parlar, Esra Sarac


Feature selection methods aim to improve the classification performance by eliminating non-valuable features. In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis. We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier. Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.

DOI: 10.5755/j01.eie.25.1.22736


Feature selection; Machine learning; Natural language processing; Text mining; Sentiment analysis.

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Print ISSN: 1392-1215
Online ISSN: 2029-5731