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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.

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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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