On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis
Asriyanti Indah Pratiwi, Adiwijaya Adiwijaya · Applied Computational Intelligence and Soft Computing · 2018
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.