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Diffusion of latent semantic analysis as a research tool: A social network analysis approach

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  • Tonta, Yaşar
  • Darvish, Hamid R.

Abstract

Latent semantic analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from information retrieval to machine learning and so on. In this paper, we chart the development and diffusion of LSA as a research tool using social network analysis (SNA) approach that reveals the social structure of a discipline in terms of collaboration among scientists. Using Thomson Reuters’ Web of Science (WoS), we identified 65 papers with “latent semantic analysis” in their titles and 250 papers in their topics (but not in titles) between 1990 and 2008. We then analyzed those papers using bibliometric and SNA techniques such as co-authorship and cluster analysis. It appears that as the emphasis moves from the research tool (LSA) itself to its applications in different fields, citations to papers with LSA in their titles tend to decrease. The productivity of authors fits Lotka's Law while the network of authors is quite loose. Networks of journals cited in papers with LSA in their titles and topics are well connected.

Suggested Citation

  • Tonta, Yaşar & Darvish, Hamid R., 2010. "Diffusion of latent semantic analysis as a research tool: A social network analysis approach," Journal of Informetrics, Elsevier, vol. 4(2), pages 166-174.
    • Handle: RePEc:eee:infome:v:4:y:2010:i:2:p:166-174
    DOI: 10.1016/j.joi.2009.11.003
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    References listed on IDEAS

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    Cited by:

    1. Bingke Zhu & Hao Fan & Bingbing Xie & Ran Su & Chaofeng Zhou & Jianping He, 2020. "Mapping the Scientific Research on Healthcare Workers’ Occupational Health: A Bibliometric and Social Network Analysis," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
    2. Yu Cheng Lin & Sang Do Park, 2023. "Effects of FDI, External Trade, and Human Capital of the ICT Industry on Sustainable Development in Taiwan," Sustainability, MDPI, vol. 15(14), pages 1-24, July.
    3. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    4. Romero-Silva, Rodrigo & de Leeuw, Sander, 2021. "Learning from the past to shape the future: A comprehensive text mining analysis of OR/MS reviews," Omega, Elsevier, vol. 100(C).
    5. Farshad Madani, 2015. "‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 323-335, October.
    6. Sohrabi, Babak & Khalilijafarabad, Ahmad, 2018. "Systematic method for finding emergence research areas as data quality," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 280-287.

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