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A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research

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  • Jean-Charles Lamirel

    (LORIA, INRIA-TALARIS Project)

Abstract

The objective of this paper is to propose a new unsupervised incremental approach in order to follow the evolution of research themes for a given scientific discipline in terms of emergence or decline. Such behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspects of information by opening the way to the detection of convergences and divergences of research themes at a large scale.

Suggested Citation

  • Jean-Charles Lamirel, 2012. "A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 151-166, October.
    • Handle: RePEc:spr:scient:v:93:y:2012:i:1:d:10.1007_s11192-012-0771-0
    DOI: 10.1007/s11192-012-0771-0
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    References listed on IDEAS

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    1. Edgar Schiebel & Marianne Hörlesberger & Ivana Roche & Claire François & Dominique Besagni, 2010. "An advanced diffusion model to identify emergent research issues: the case of optoelectronic devices," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 765-781, June.
    2. S. E. Robertson & K. Sparck Jones, 1976. "Relevance weighting of search terms," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(3), pages 129-146, May.
    3. Jean-Charles Lamirel & Claire Francois & Shadi Al Shehabi & Martial Hoffmann, 2004. "New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 445-562, August.
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    Cited by:

    1. Hasnat, Md. Abul & Velcin, Julien & Bonnevay, Stephane & Jacques, Julien, 2017. "Evolutionary clustering for categorical data using parametric links among multinomial mixture models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 141-159.
    2. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    3. Xie, Qing & Zhang, Xinyuan & Ding, Ying & Song, Min, 2020. "Monolingual and multilingual topic analysis using LDA and BERT embeddings," Journal of Informetrics, Elsevier, vol. 14(3).
    4. Anke Piepenbrink & Elkin Nurmammadov, 2015. "Topics in the literature of transition economies and emerging markets," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2107-2130, March.

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