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Topic based research competitiveness evaluation

Author

Listed:
  • Tingcan Ma

    (Chinese Academy of Sciences)

  • Ruinan Li

    (Chinese Academy of Sciences)

  • Guiyan Ou

    (Chinese Academy of Sciences)

  • Mingliang Yue

    (Chinese Academy of Sciences)

Abstract

Research competitiveness analysis refers to the measurement, comparison and analysis of the research status (i.e., strength and/or weakness) of different scientific research bodies (e.g., institutions, researchers, etc.) in different research fields. Improving research competitiveness analysis method can be conducive to accurately obtaining the research status of research fields and research bodies. This paper presents a method of evaluating the competitiveness of research institutions based on research topic distribution. The method uses the LDA topic model to obtain a paper-topic distribution matrix to objectively assign the academic impact of papers (such as number of citations) to research topics. Then the method calculates the competitiveness of each research institution on each research topic with the help of an institution-paper matrix. Finally, the competitiveness and the research strength and/or weakness of the institutions are defined and characterized. A case study shows that the method can lead to an objective and effective evaluation of the research competitiveness of research institutions in a given research field.

Suggested Citation

  • Tingcan Ma & Ruinan Li & Guiyan Ou & Mingliang Yue, 2018. "Topic based research competitiveness evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 789-803, November.
  • Handle: RePEc:spr:scient:v:117:y:2018:i:2:d:10.1007_s11192-018-2891-7
    DOI: 10.1007/s11192-018-2891-7
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    References listed on IDEAS

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    1. Tânia F. G. G. Cova & Alberto A. C. C. Pais & Sebastião J. Formosinho, 2013. "Iberian universities: a characterisation from ESI rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1239-1251, March.
    2. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    3. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    4. Steven A. Morris & G. Yen & Zheng Wu & Benyam Asnake, 2003. "Time line visualization of research fronts," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 413-422, March.
    5. Henry Small & Phineas Upham, 2009. "Citation structure of an emerging research area on the verge of application," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(2), pages 365-375, May.
    6. Arho Suominen & Hannes Toivanen, 2016. "Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(10), pages 2464-2476, October.
    7. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    8. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
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    Cited by:

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    3. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.

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