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Combining full-text analysis and bibliometric indicators. A pilot study

Author

Listed:
  • Patrick Glenisson

    (Katholieke Universiteit Leuven, Steunpunt O&O Statistieken)

  • Wolfgang Glänzel

    (Katholieke Universiteit Leuven, Steunpunt O&O Statistieken)

  • Olle Persson

    (Inforsk, Department of Sociology, Umeå University)

Abstract

Summary In the present study full-text analysis and traditional bibliometric methods are combined to improve the efficiency of the individual methods in the mapping of science. The methodology is applied to map research papers from a special issue of Scientometrics. The outcomes substantiate that such hybrid methodology can be applied to both research evaluation and information retrieval. The subject classification given by the guest-editors of the special issue is used for validation purposes. Because of the limited number of papers underlying the study the paper is considered a pilot study that will be extended in a later study on the basis of a larger corpus.

Suggested Citation

  • Patrick Glenisson & Wolfgang Glänzel & Olle Persson, 2005. "Combining full-text analysis and bibliometric indicators. A pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(1), pages 163-180, March.
    • Handle: RePEc:spr:scient:v:63:y:2005:i:1:d:10.1007_s11192-005-0208-0
    DOI: 10.1007/s11192-005-0208-0
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    Citations

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    Cited by:

    1. Edoardo Magnone, 2014. "A novel graphical representation of sentence complexity: the description and its application," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1301-1329, February.
    2. Tom Magerman & Bart Looy & Xiaoyan Song, 2010. "Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 289-306, February.
    3. Antje Klitkou & Stian Nygaard & Martin Meyer, 2007. "Tracking techno-science networks: A case study of fuel cells and related hydrogen technology R&D in Norway," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(2), pages 491-518, February.
    4. Guillaume Cabanac, 2011. "Accuracy of inter-researcher similarity measures based on topical and social clues," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 597-620, June.
    5. 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.
    6. Muhammad Kamran Abbasi & Ingo Frommholz, 2015. "Cluster-based polyrepresentation as science modelling approach for information retrieval," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2301-2322, March.
    7. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    8. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2018. "Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 869-892, May.
    9. Dahui Dong & Meng-Lin Chen, 2015. "Publication trends and co-citation mapping of translation studies between 2000 and 2015," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1111-1128, November.
    10. Cristian Colliander & Per Ahlgren, 2012. "Experimental comparison of first and second-order similarities in a scientometric context," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 675-685, February.
    11. Lucie Beranová & Marcin P. Joachimiak & Tomáš Kliegr & Gollam Rabby & Vilém Sklenák, 2022. "Why was this cited? Explainable machine learning applied to COVID-19 research literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2313-2349, May.

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