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The International Conference on Decision Economics

DECON 2020: Decision Economics: Minds, Machines, and their Society pp 140–150Cite as

Research on Burst Terms Detection Based on Entropy Weight Method

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Part of the Studies in Computational Intelligence book series (SCI,volume 990)

Abstract

Since burst terms are forward-looking and informative, burst term detection (BTD) helps to predict emerging research trends in certain subject areas. However, currently several BTD models are short of simple operation and objective identification. In this paper, we propose a burst term detection model based on entropy weight (entropy-BTD). Under the model, terms were classified into various burst levels by clustering entropy-variation, and the calculation formula of burst comprehensive value was deduced. Experiments showed that three burst classes were identified from clustering: sudden-burst terms, strong-burst terms, and weak-burst terms. The burst terms with high-credibility burst and positive trend were deleted.

Keywords

  • Burst terms
  • Information entropy
  • Burst detection
  • K-means cluster

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  • DOI: 10.1007/978-3-030-75583-6_14
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Acknowledgments

The work was supported by Guangzhou Science and technology plan project “Theory, method and application of Burst Terms Detection” (Grant numbers 202002030384). This is an outcome of the project “Research on Domain Hot Spot Detection Theory, Method and Application” (No. 2019WKXMO043) supported by Graduate Innovation Project of South China Normal University.

Author information

Authors and Affiliations

  1. School of Economics and Management, South China Normal University, Guangzhou, 510006, China

    Guo-he Feng & Xing-qing Mo

  2. Business School, Department of Information Resources Management, Nankai University, Tianjin, 300071, China

    Yong-xin Kong

Authors
  1. Guo-he Feng
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  2. Yong-xin Kong
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  3. Xing-qing Mo
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Editor information

Editors and Affiliations

  1. Department of PPEQS, Section of Economics and Quantitative Methods, University of Chieti-Pescara, Pescara, Pescara, Italy

    Edgardo Bucciarelli

  2. Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  3. AIR Institute - Deep Tech Lab, University of Valladolid, Valladolid, Spain

    Juan M. Corchado

  4. Department of Business and Economics Adminstration, University of Salamanca, Salamanca, Spain

    Javier Parra D.

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Feng, Gh., Kong, Yx., Mo, Xq. (2021). Research on Burst Terms Detection Based on Entropy Weight Method. In: Bucciarelli, E., Chen, SH., Corchado, J.M., Parra D., J. (eds) Decision Economics: Minds, Machines, and their Society. DECON 2020. Studies in Computational Intelligence, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-030-75583-6_14

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