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International Conference in Communications, Signal Processing, and Systems

CSPS 2018: Communications, Signal Processing, and Systems pp 168–174Cite as

Short-Text Sentiment Analysis Based on Windowed Word Vector

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 517))

Abstract

The traditional text sentiment analysis directly inputs syntactic feature or word vector to model. It fails to consider the characteristics of time series in sentiment. Considering that product reviews are short text, this paper comes up with the method of windowed word vector and classifier fusion in the decision layer. The results indicate that the proposed method can achieve better performance than several existing methods.

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Acknowledgements

The paper is supported by the National Natural Science Foundation (61673108), China Postdoctoral Science Foundation (2016M601695), and Jiangsu University Natural Science Research Project (17KJB510018).

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Authors and Affiliations

  1. School of Physics and Electronic Engineering, Jiangsu Normal University, No. 101, Shanghai Road, Xuzhou, 221116, Tongshan District, Jiangsu Province, People’s Republic of China

    Dongmei Zhao, Yingli Shen, Yabo Shen, Yong Ma, Yun Jin, Shidang Li & Mingliang Gu

  2. The key laboratory of children development and learning science, ministry of education, Southeast University, Nanjing, China

    Yun Jin

Authors
  1. Dongmei Zhao
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  2. Yingli Shen
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  3. Yabo Shen
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  4. Yong Ma
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  5. Yun Jin
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  6. Shidang Li
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  7. Mingliang Gu
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Corresponding author

Correspondence to Mingliang Gu .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2020 Springer Nature Singapore Pte Ltd.

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Cite this paper

Zhao, D. et al. (2020). Short-Text Sentiment Analysis Based on Windowed Word Vector. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_22

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  • DOI: https://doi.org/10.1007/978-981-13-6508-9_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6507-2

  • Online ISBN: 978-981-13-6508-9

  • eBook Packages: EngineeringEngineering (R0)

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