Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2018, Article ID 1407817, 5 pages
Research Article

On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

Telkom University, Telekomunikasi Street No. 1, Bandung 40257, Indonesia

Correspondence should be addressed to Asriyanti Indah Pratiwi; moc.liamg@iwitarphadniitnayirsa

Received 10 July 2017; Revised 9 October 2017; Accepted 26 November 2017; Published 19 February 2018

Academic Editor: Rodolfo Zunino

Copyright © 2018 Asriyanti Indah Pratiwi and Adiwijaya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of analysis slow and less sensitive. Finding the optimum feature selection and classification is still a challenge. In order to handle an enormous number of features and provide better sentiment classification, an information-based feature selection and classification are proposed. The proposed method reduces more than 90% unnecessary features while the proposed classification scheme achieves 96% accuracy of sentiment classification. From the experimental results, it can be concluded that the combination of proposed feature selection and classification achieves the best performance so far.