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A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Author

Listed:
  • Sehrish Iqbal

    (Information Technology University)

  • Saeed-Ul Hassan

    (Information Technology University)

  • Naif Radi Aljohani

    (King Abdulaziz University)

  • Salem Alelyani

    (King Khalid University
    King Khalid University)

  • Raheel Nawaz

    (Manchester Metropolitan University)

  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

Abstract

In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.

Suggested Citation

  • Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04055-1
    DOI: 10.1007/s11192-021-04055-1
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    References listed on IDEAS

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    Cited by:

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    5. Yunxue Cui & Yongzhen Wang & Xiaozhong Liu & Xianwen Wang & Xuhong Zhang, 2023. "Multidimensional scholarly citations: Characterizing and understanding scholars' citation behaviors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 115-127, January.

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