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

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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.

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Notes

  1. http://ganj.irandoc.ac.ir.

  2. Available in: http://thesauri.irandoc.ac.ir/.

  3. The fraction of the records that are relevant to the query that are successfully retrieved.

  4. The fraction of retrieved records that are relevant to the query.

  5. An open source engine for full text search.

  6. Boolean Operators are used to connect and define the relationship between search terms (e.g. +,−, &, not, etc.).

  7. Popular but invaluable words (same as “the”, “in”, “as”… in English).

  8. The Islamic theory or philosophy of law.

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Acknowledgements

The authors gratefully acknowledge the support of the Iranian Research Institute for Information Science and Technology (IRANDOC). And specially acknowledge the help of IRANDOC’s R&D department.

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Authors and Affiliations

  1. Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, 131573314, Iran

    Mohammad Rabiei

  2. School of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Mohammad Rabiei, Seyyed-Mahdi Hosseini-Motlagh & Abdorrahman Haeri

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  1. Mohammad Rabiei
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  2. Seyyed-Mahdi Hosseini-Motlagh
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  3. Abdorrahman Haeri
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Correspondence to Seyyed-Mahdi Hosseini-Motlagh.

Appendix

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Table 7 Original Persian list of top 10 frequent N-grams in users’ Queries
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Rabiei, M., Hosseini-Motlagh, SM. & Haeri, A. Using text mining techniques for identifying research gaps and priorities: a case study of the environmental science in Iran. Scientometrics 110, 815–842 (2017). https://doi.org/10.1007/s11192-016-2195-8

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