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Using text mining techniques for identifying research gaps and priorities: a case study of the environmental science in Iran

Author

Listed:
  • Mohammad Rabiei

    (Iranian Research Institute for Information Science and Technology (IranDoc)
    Iran University of Science and Technology (IUST))

  • Seyyed-Mahdi Hosseini-Motlagh

    (Iran University of Science and Technology (IUST))

  • Abdorrahman Haeri

    (Iran University of Science and Technology (IUST))

Abstract

This study aims to observe the researchers’ behavior in Iranian scientific databases to determine the research gaps and priorities in their field of research. Text mining and natural language processing techniques were used to identify what researchers are looking for and to analyze existing research works. In this paper, the information about the behavior of researchers who work in the field of environmental science and existing research works in the Iranian scientific database are processed. The search trends in all areas are evaluated by analyzing the users’ search data. The trend analysis indicates that in the period of February 2013 to July 2015, the growth of the researchers’ requests in some domains of the environment such as Industry, Training, Assessment, Material, Water and Pollution was 1.5 up to 2 times more than the overall requests. A Combination of the trend analysis and clustering of queries led to shaping four priority zones. Then, the research priorities for each environmental research area were determined. The results show that Training, Pollution, Rangeland, Management and Law are those domains in the environmental research which have the most research gaps in Iran, but there are enough research in Forest, Soil and Industry domains. At the end, we describe the steps for the implementation of a decision support system in environmental research management. Researchers, managers and policy makers can use this proposed “research demand and supply monitoring” system or RDSM to make appropriate decisions and allocate their resources more efficiently.

Suggested Citation

  • Mohammad Rabiei & Seyyed-Mahdi Hosseini-Motlagh & Abdorrahman Haeri, 2017. "Using text mining techniques for identifying research gaps and priorities: a case study of the environmental science in Iran," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 815-842, February.
    • Handle: RePEc:spr:scient:v:110:y:2017:i:2:d:10.1007_s11192-016-2195-8
    DOI: 10.1007/s11192-016-2195-8
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    References listed on IDEAS

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    1. Danuta Piróg & Joanna Fidelus-Orzechowska & Łukasz Wiejaczka, 2022. "Local Authority vs Community Visions of Dam Project Land Development: a Text Mining Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1833-1848, April.
    2. Abdorrahman Haeri, 2020. "Analyzing safety level and recognizing flaws of commercial centers through data mining approach," Journal of Risk and Reliability, , vol. 234(3), pages 512-526, June.
    3. Xiaolong Xue & Liang Wang & Rebecca J. Yang, 2018. "Exploring the science of resilience: critical review and bibliometric analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 477-510, January.
    4. Rongjiang Cai & Tao Lv & Xu Deng, 2021. "Evaluation of Environmental Information Disclosure of Listed Companies in China’s Heavy Pollution Industries: A Text Mining-Based Methodology," Sustainability, MDPI, vol. 13(10), pages 1-23, May.

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