IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v128y2023i3d10.1007_s11192-022-04618-w.html
   My bibliography  Save this article

A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information

Author

Listed:
  • Najmeh Masoumi

    (Ferdowsi University of Mashhad)

  • Reza Khajavi

    (Ferdowsi University of Mashhad)

Abstract

Assessment of new research topics and emerging technologies in any branch of knowledge is important for researchers, universities and research institutes, research investors, industry sectors, and scientific policymakers for a variety of reasons. The basic premise of this research is that the topics of interest for academic research are those that are yet underdeveloped, but are relatively well sponsored by investors. This paper proposes a method to identify and evaluate topics for their research, industrial and commercial potential based on development, investment and investment-to-development ratio (investment appeal). Since the target audience of this paper is researchers in all fields of knowledge who are mostly unfamiliar with scientometric schemes, the proposed method is aimed to be simple, based on meta-databases with easy access, without any need to clustering on keywords. The development index is defined as the keyword link strength obtained from the keyword co-occurrence network, and investment is introduced as the number of sponsors associated with each keyword. From the qualitative analysis of the development-investment diagram, six sets of keywords, entitled as: for Research, for Industry, for Commerce, Matured, Academic and Chaotic, are identified. Due to uncertain membership of research topics to these sets and their relative overlapping, they are defined as fuzzy sets. A fuzzy model, called as Fuzzy Research Ranking System (FRRS), is designed to characterize the fuzzy behavior of research topics and their potential assessment, the output of which is the membership of keywords to any of the six predefined fuzzy sets. The proposed method has been implemented for a sample knowledge domain, Geo-Engineering, which is an interdisciplinary field with significant technological capacity. Expert review of the results shows that the method is relatively well qualified for its ability to identify research topics with technological and industrial perspectives from purely scientific keywords, and may efficiently introduce a ranked list of research topics to the researchers.

Suggested Citation

  • Najmeh Masoumi & Reza Khajavi, 2023. "A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1485-1512, March.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:3:d:10.1007_s11192-022-04618-w
    DOI: 10.1007/s11192-022-04618-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04618-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-022-04618-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    2. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    3. Hsin-Ning Su & Pei-Chun Lee, 2010. "Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 65-79, October.
    4. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    5. Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    6. Shen, Yung-Chi & Wang, Ming-Yeu & Yang, Ya-Chu, 2020. "Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    7. Nadezhda Mikova & Anna Sokolova, 2014. "Global technology trends monitoring: theoretical Frameworks and best practices," Foresight-Russia Форсайт, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 8(4 (eng)), pages 64-83.
    8. Ho, Jonathan C. & Saw, Ewe-Chai & Lu, Louis Y.Y. & Liu, John S., 2014. "Technological barriers and research trends in fuel cell technologies: A citation network analysis," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 66-79.
    9. Wang, Ming-Yeu & Fang, Shih-Chieh & Chang, Yu-Hsuan, 2015. "Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 182-195.
    10. J. A. García & Rosa Rodriguez-Sánchez & J. Fdez-Valdivia & J. Martinez-Baena, 2012. "On first quartile journals which are not of highest impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 925-943, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    2. Rezaeian, M. & Montazeri, H. & Loonen, R.C.G.M., 2017. "Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 270-280.
    3. Shen, Yung-Chi & Wang, Ming-Yeu & Yang, Ya-Chu, 2020. "Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    4. Kai Hu & Huayi Wu & Kunlun Qi & Jingmin Yu & Siluo Yang & Tianxing Yu & Jie Zheng & Bo Liu, 2018. "A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1031-1068, March.
    5. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    6. Ba, Zhichao & Meng, Kai & Ma, Yaxue & Xia, Yikun, 2024. "Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    7. Munan Li, 2018. "Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 77-100, July.
    8. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    9. Yasutomo Takano & Yuya Kajikawa & Makoto Ando, 2017. "Trends and Typology of Emerging Antenna Propagation Technologies: Citation Network Analysis," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 1-19, February.
    10. Ju, Yonghan & Sohn, So Young, 2015. "Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 44-64.
    11. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    12. Linares, Ian Marques Porto & De Paulo, Alex Fabianne & Porto, Geciane Silveira, 2019. "Patent-based network analysis to understand technological innovation pathways and trends," Technology in Society, Elsevier, vol. 59(C).
    13. Alba Santa Soriano & Carolina Lorenzo Álvarez & Rosa María Torres Valdés, 2018. "Bibliometric analysis to identify an emerging research area: Public Relations Intelligence—a challenge to strengthen technological observatories in the network society," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1591-1614, June.
    14. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    15. Kang, Inje & Yang, Jiseong & Lee, Wonjae & Seo, Eun-Yeong & Lee, Duk Hee, 2023. "Delineating development trends of nanotechnology in the semiconductor industry: Focusing on the relationship between science and technology by employing structural topic model," Technology in Society, Elsevier, vol. 74(C).
    16. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    17. Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    18. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    19. Jian Xu & Yi Bu & Ying Ding & Sinan Yang & Hongli Zhang & Chen Yu & Lin Sun, 2018. "Understanding the formation of interdisciplinary research from the perspective of keyword evolution: a case study on joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 973-995, November.
    20. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:128:y:2023:i:3:d:10.1007_s11192-022-04618-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.