Introducing Facetometrics: A New Keyword-Based Measure for Locating Research Areas of a Subject

Jana, Kalipada and Dutta, Bidyarthi Introducing Facetometrics: A New Keyword-Based Measure for Locating Research Areas of a Subject. SRELS Journal of Information Management, 2016, vol. 53, n. 3, pp. 177-185. [Journal article (Paginated)]

[img] Text
Paper-SRELS-Jana-Dutta.pdf

Download (357kB)

English abstract

This paper has introduced a technique to find out research areas in a subject by the analysis of keywords assigned to articles. Keywords assigned to 1227 research papers published between 2004 and 2013 in a specific subject area “Hawking radiation” have been collected and analyzed. The research articles were retrieved from Web of Science using the search term “Hawking Radiation”. The assigned keywords occurred with different frequencies. The keywords formed the clusters of words. The names given to the clusters were in accordance with the names of the most frequently occurring word in a keyword clusters. The clusters have been classed into three groups by size, i.e. small cluster, medium cluster and large cluster. As fairly large number of keywords formed large clusters, it has been assumed that the potential facets are represented by such clusters. Three basic parameters associated with the keyword clusters were identified, viz. no. of keywords in a cluster, frequency of occurrence and occupancy. Four indicators, viz., stability index, integrated visibility index, momentary visibility index and potency index have been defined on the basis of these three parameters and their fluctuations over the study period have been noticed. These indicators hold different values for different clusters and facets. The value ranges of these are categorized in five groups, viz. very high, high, medium, low and very low. Each

Item type: Journal article (Paginated)
Keywords: Keywords: Facetometrics, Keyword Cluster, Hawking Radiation, Research Areas, Web of Science, Astrophysics Research Trend, Research
Subjects: A. Theoretical and general aspects of libraries and information.
A. Theoretical and general aspects of libraries and information. > AA. Library and information science as a field.
I. Information treatment for information services > IB. Content analysis (A and I, class.)
I. Information treatment for information services > ID. Knowledge representation.
Depositing user: Bidyarthi Dutta
Date deposited: 24 Jun 2016 06:01
Last modified: 24 Jun 2016 06:01
URI: http://hdl.handle.net/10760/29513

References

7. References 1. Berelson, B. 1952. Content Analysis in Communications Research. New York: Free Press. 2. Black, J.D. 1962. The Keyword: It’s use in abstracting, indexing and retrieving information. Aslib Proceedings. 14(10): 313–321. 3. Cambrosio, A., et al. 1993. Historical scientometrics? Mapping over 70 years of biological safety research with Co-word analysis. Scientometrics. 27(2): 119–143. 4. Coulter, N., Monarch, I. & Konda, S. 1998. Software engineering as seen through its research literature: A study in co-word analysis. Journal of the American Society for Information Science. 49(13): 1206–1223. 5. Courtial, J.P. 1994. (A) Co-word analysis of scientometrics. Scientometrics. 31(3): 251–260. 6. Courtial, J.P., Cahlik, T. & Callon, M. 1994. (A) Model for social interaction between cognition and action through a key-word simulation of knowledge growth. Scientometrics. 31(2): 173–192. 7. Dutta, B. 2008. An analytical investigation into the characteristics of a subject - A case study. PhD thesis. Jadavpur University.

8. Dutta, B., Majumder, K.P. & Sen, B.K. 2008. Classification of keywords extracted from research articles published in science journals. Annals of Library and Information Studies. 55(4): 317–333. 9. Dutta, B., Majumder, K.P., Sen, B.K. 2010. Content management by keywords: an analytical study. SRELS Journal of Information Management. 47(6): 599–620. 10. Hartley, J. & Kostoff, R.N. 2003. How useful are ‘keywords’ in scientific journals? Journal of Information Science. 29(5): 433–438. 11. Index Term. Retrieved on 16 Jan 2016 from https:// en.wikipedia.org/wiki/Index_term 12. Retrieved on 5 Mar 2015 from http://shodhganga.inflibnet. ac.in/bitstream/10603/5109/10/10_chapter%201.pdf 13. Law, J. & Whittaker, J. 1992. Mapping acidification research: A test of the co-word method. Scientometrics. 23(3): 417– 461. 14. Looze, M.D. & Lemarie, J. 1997. Corpus relevance through co-word analysis: An application to plant proteins. Scientometrics. 39(3): 267–280. 15. Luhn, H.P. 1960. Keyword-in-Context for technical literature (KWIC Index). American Documentation. 11. 16. Noyons, E.C.M. & van Raan, A.F.J. 1998. Advanced mapping of science and technology. Scientometrics. 41(1–2): 61–67. 17. Noyons, E.C.M. & van Raan, A.F.J. 1998. Monitoring scientific developments from a dynamic perspective: Self- organized structuring to map neural network research. Journal of the American Society for Information Science. 49(1): 68–81. 18. de Solla Price, D.J. 1963. Little science, big science. New York: Columbia University Press. 19. de Solla Price, D.J. 1975. Science since Babylon. Enlarged ed. New Haven: Yale University Press; p. 165–186. 20. Seetharama, S. 1997. Information consolidation and repackaging: framework, methodology, planning. New Delhi: Ess Ess Pub. 21. Van Raan, A.F.J., & Tijssen, R.J.W. 1993. The neural net of neural network research: An exercise in bibliometric mapping. Scientometrics. 26(1): 169–192. 22. Van Raan, A.F.J. 1997. Scientometrics: State-of-the-art. Scientometrics. 38(1): 205–218. 23. Willett, P. 1988. Recent trends in hierarchical document clustering: A critical review. Information Processing and Management. 24(5): 577–597.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item