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Optimal Feature Selection for Learning-Based Algorithms for Sentiment Classification

  • Zhaoxia WangEmail author
  • Zhiping Lin
Article
  • 32 Downloads

Abstract

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment classification performance of the learning-based methods. Therefore, we investigate the relationship between the number of features selected and the sentiment classification performance of the learning-based methods. A new method for the selection of a suitable number of features is proposed in which the Chi Square feature selection algorithm is employed and the features are selected using a preset score threshold. It is discovered that there is a relationship between the logarithm of the number of features selected and the sentiment classification performance of the learning-based method, and it is also found that this relationship is independent of the learning-based method involved. The new findings in this research indicate that it is always possible for researchers to select the appropriate number of features for learning-based methods to obtain the best sentiment classification performance. This can guide researchers to select the proper features for optimizing the performance of learning-based algorithms. (A preliminary version of this paper received a Best Paper Award at the International Conference on Extreme Learning Machines 2018.)

Keywords

Machine learning Feature selection Optimal feature selection Relationship analysis Sentiment classification Social media Text analysis 

Notes

Acknowledgments

The authors would like to thank Dr. Ho Seng Beng, Dr. Quek Boon Kiat, and the team of A*STAR AI program for their discussion and help. The authors would also like to thank the intern students from NTU and SUTD for the assistance in this research.

Compliance with Ethical Standards

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information SystemsSingapore Management University (SMU)SingaporeSingapore
  2. 2.Nanjing University of Information Science and Technology (NUIST)NanjingChina
  3. 3.Institute of High Performance Computing (IHPC)Agency for Science, Technology and Research (A*STAR)SingaporeSingapore
  4. 4.School of Electrical and Electronic Engineering (EEE)Nanyang Technological UniversitySingaporeSingapore

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