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Extraction and Tracking of Scientific Topics by LDA

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

Scientific papers play an important role in the scientific research. Scientific researchers understand the research trends of their interested scientific topics. But the huge amount of papers makes them difficult to get a global view of scientific topics. In this paper, we proposed an automatic LDA-based method to extract scientific topics and track their evolutions. The method firstly divides the papers into some subsets according to the paper’s published year, then extracts scientific topics for each subset. To tract the evolutions of topics and their relations, a directed graph is constructed in terms of KL distances. The experimental results on Amind ACM-Citation-network dataset shows that our method is reasonable.

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Acknowledgments

This work was supported by the Chinese National Natural Science Foundation (Grant No.: 61602202), the Natural Science Foundation of Jiangsu Province, China (Grant No.: BK20160428), the Social Key Research and Development Project of Huaian, Jiangsu, China (Grant No.: HAS2015020) and the Graduate Student Scientific Research and Innovation Project of Jiangsu Province, China (Grant No.: 2015B38314).

Author information

Authors and Affiliations

  1. Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China

    Yongjun Zhang, Jialin Ma & Bolun Chen

  2. College of Computer and Information, Hohai University, Nanjing, China

    Yongjun Zhang, Jialin Ma & Zijian Wang

Authors
  1. Yongjun Zhang
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  2. Jialin Ma
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  3. Zijian Wang
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  4. Bolun Chen
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Corresponding author

Correspondence to Yongjun Zhang .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

  3. School of Business, UNSW Australia, Canberra, Aust Capital Terr, Australia

    Omar Khadeer Hussain

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Zhang, Y., Ma, J., Wang, Z., Chen, B. (2018). Extraction and Tracking of Scientific Topics by LDA. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_48

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  • DOI: https://doi.org/10.1007/978-3-319-65636-6_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

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