Application of Rough Set-Based Feature Selection for Arabic Sentiment Analysis

Application of Rough Set-Based Feature Selection for Arabic Sentiment Analysis Sentiment analysis is considered as one of the recent applications of text categorization that categories the emotions expressed in text as negative, positive, and natural. Rough set theory is a mathematical tool used to analyze uncertainty, incomplete information, and data reduction. Indiscernibility, reduct, and core are essential concepts in rough set theory that can be employed for data classification and knowledge reduction. This paper proposes to use the rough set-based methods for sentiment analysis to classify tweets that are written in the Arabic language. The paper investigates the application of the reduct concept of rough set theory as a feature selection method for sentiment analysis. This paper investigates four reduct computation techniques to generate the set of reducts. For classification purposes, two rule generation algorithms have been studied to build the rough set rule-based classifier. An Arabic data set of 4800 tweets is used in the experiments to validate the use of reduct computation for Arabic sentiment analysis. The results of the experiments showed that using rough set reducts techniques lead to different results and some of them can perform better than non-rough set classifier. The best classification accuracy rate was for rough set classifier using the full attribute weighting reduct generation algorithm which achieved an accuracy of 74%. The primary results indicate that using the rough set theory framework for sentiment analysis is an appealing option where it can enhance the overall accuracy and reduce the number of used terms for classification which in turn will lead to a faster classification process, especially with a large dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cognitive Computation Springer Journals

Application of Rough Set-Based Feature Selection for Arabic Sentiment Analysis

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Computation by Abstract Devices; Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics
ISSN
1866-9956
eISSN
1866-9964
D.O.I.
10.1007/s12559-017-9477-1
Publisher site
See Article on Publisher Site

Abstract

Sentiment analysis is considered as one of the recent applications of text categorization that categories the emotions expressed in text as negative, positive, and natural. Rough set theory is a mathematical tool used to analyze uncertainty, incomplete information, and data reduction. Indiscernibility, reduct, and core are essential concepts in rough set theory that can be employed for data classification and knowledge reduction. This paper proposes to use the rough set-based methods for sentiment analysis to classify tweets that are written in the Arabic language. The paper investigates the application of the reduct concept of rough set theory as a feature selection method for sentiment analysis. This paper investigates four reduct computation techniques to generate the set of reducts. For classification purposes, two rule generation algorithms have been studied to build the rough set rule-based classifier. An Arabic data set of 4800 tweets is used in the experiments to validate the use of reduct computation for Arabic sentiment analysis. The results of the experiments showed that using rough set reducts techniques lead to different results and some of them can perform better than non-rough set classifier. The best classification accuracy rate was for rough set classifier using the full attribute weighting reduct generation algorithm which achieved an accuracy of 74%. The primary results indicate that using the rough set theory framework for sentiment analysis is an appealing option where it can enhance the overall accuracy and reduce the number of used terms for classification which in turn will lead to a faster classification process, especially with a large dataset.

Journal

Cognitive ComputationSpringer Journals

Published: Jun 3, 2017

References

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