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Short-Text Sentiment Analysis Based on Windowed Word Vector

  • Dongmei Zhao
  • Yingli Shen
  • Yabo Shen
  • Yong Ma
  • Yun Jin
  • Shidang Li
  • Mingliang GuEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (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.

Keywords

Sentiment analysis Windowed word vector Classifier fusion Short text 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dongmei Zhao
    • 1
  • Yingli Shen
    • 1
  • Yabo Shen
    • 1
  • Yong Ma
    • 1
  • Yun Jin
    • 1
    • 2
  • Shidang Li
    • 1
  • Mingliang Gu
    • 1
    Email author
  1. 1.School of Physics and Electronic EngineeringJiangsu Normal UniversityXuzhouPeople’s Republic of China
  2. 2.The key laboratory of children development and learning science, ministry of educationSoutheast UniversityNanjingChina

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