On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis
Joint Authors
Pratiwi, Asriyanti Indah
Adiwijaya, Asriyanti Indah
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-19
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Pratiwi, Asriyanti Indah& Adiwijaya, Asriyanti Indah. 2018. On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis. Applied Computational Intelligence and Soft Computing،Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1117037
Modern Language Association (MLA)
Pratiwi, Asriyanti Indah& Adiwijaya, Asriyanti Indah. On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis. Applied Computational Intelligence and Soft Computing No. 2018 (2018), pp.1-5.
https://search.emarefa.net/detail/BIM-1117037
American Medical Association (AMA)
Pratiwi, Asriyanti Indah& Adiwijaya, Asriyanti Indah. On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis. Applied Computational Intelligence and Soft Computing. 2018. Vol. 2018, no. 2018, pp.1-5.
https://search.emarefa.net/detail/BIM-1117037
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1117037