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An Intelligent Logistic Regression Approach for Verb Expression’s Sentiment Analysis

  • Daoning Jiang
  • Qian TaoEmail author
  • Zhenyu Wang
  • Lixia Dong
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 752)

Abstract

Sentiment analysis of text has tremendous value in many fields. But verb expression is absent, while lots of researchers concentrate on identifying opinions from adjective, adverb, and noun expressions in recent years. In this paper, we find that verb expressions in a sentence can be more important because verb expressions not only imply opinions but also give a direct way for enterprise to improve their products. It is meaningful that the verb expressions are extracted and analyzed. In order to deal with this problem, we propose a new method of linear regression optimized by particle swarm optimization to analyze verb expression extracted from reviews. Since our training data is obtained from titles of reviews whose labels are automatically inferred from review ratings, our method is able to work without manual involvement. Experimental results demonstrate our approach has great performance in terms of both precision and efficiency.

Keywords

Verb expression Sentiment analysis Particle swarm optimization Multiple linear regression Features engineering 

Notes

Acknowledgements

This work is supported in part by 2017 Central Universities Fundamental Research Project (General Program ID:56),in part by the Guangdong Province Natural Science Foundation under Grant No. 2015A030310194, in part by the Guangdong Province Science and Technology Project under Grant No. 2015A080804019 and No. 2015B010131003, in part by the Guangzhou Municipal Science and Technology Project under Grant No. 2015SX017 and No. 2015SX022, in part by the Guangdong Province Colleges Major Projects under Grant No. 2014KTSCX194, in part by the surface project of Wuxi health and family planning No. MS201716, in part by the Wuxi Science and Technology Development Guiding Project No. CSZ0N1617, and in part by 2016 NSFC-Guangdong Joint Fund of Scientific: Supercomputing Application Research Projects.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Daoning Jiang
    • 1
  • Qian Tao
    • 1
    Email author
  • Zhenyu Wang
    • 1
  • Lixia Dong
    • 2
  1. 1.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Wuxi Mental Health CenterNanjing Medical UniversityWuxiChina

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