IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04554013.html
   My bibliography  Save this paper

Le temps dans le discours, expérimentation d’un protocole d’observation des caractéristiques temporelles d’un corpus d’avis de salariés

Author

Listed:
  • Daniel Pélissier

    (IDETCOM - Institut du Droit de l'Espace, des Territoires, de la Culture et de la Communication - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

Abstract

Temps et langage sont étroitement liés. La lexicométrie a développé des méthodes pour comprendre les discours dans le temps. Cet article propose une approche complémentaire en étudiant le temps dans le langage. L'expérimentation porte sur une méthode structurée d'analyse du temps dans le discours à partir d'un corpus de données massives d'avis de salariés. Le protocole utilisé permet d'aborder le sens par les spécificités temporelles. Les limites constatées de cette approche exploratoire justifient une poursuite des recherches sur ce questionnement original.

Suggested Citation

  • Daniel Pélissier, 2022. "Le temps dans le discours, expérimentation d’un protocole d’observation des caractéristiques temporelles d’un corpus d’avis de salariés," Post-Print hal-04554013, HAL.
    • Handle: RePEc:hal:journl:hal-04554013
    Note: View the original document on HAL open archive server: https://hal.science/hal-04554013
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04554013/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaoguang Wang & Qikai Cheng & Wei Lu, 2014. "Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1253-1271, November.
    2. Martin Rosvall & Carl T Bergstrom, 2010. "Mapping Change in Large Networks," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-7, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shiji Chen & Clément Arsenault & Yves Gingras & Vincent Larivière, 2015. "Exploring the interdisciplinary evolution of a discipline: the case of Biochemistry and Molecular Biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1307-1323, February.
    2. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Ali Najmi & Taha H. Rashidi & Alireza Abbasi & S. Travis Waller, 2017. "Reviewing the transport domain: an evolutionary bibliometrics and network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 843-865, February.
    4. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    5. Jimi Adams & Ryan Light, 2014. "Mapping Interdisciplinary Fields: Efficiencies, Gaps and Redundancies in HIV/AIDS Research," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    6. Marie Katsurai & Shunsuke Ono, 2019. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1583-1598, December.
    7. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    8. Nicola Melluso & Andrea Bonaccorsi & Filippo Chiarello & Gualtiero Fantoni, 2021. "Rapid detection of fast innovation under the pressure of COVID-19," Papers 2102.00197, arXiv.org.
    9. Melissa Haller & David L. Rigby, 2020. "The geographic evolution of optics technologies in the United States, 1976–2010," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1539-1559, December.
    10. Xiaoguang Wang & Hongyu Wang & Han Huang, 2021. "Evolutionary exploration and comparative analysis of the research topic networks in information disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4991-5017, June.
    11. Raghu Raman & Nava Subramaniam & Vinith Kumar Nair & Avinash Shivdas & Krishnashree Achuthan & Prema Nedungadi, 2022. "Women Entrepreneurship and Sustainable Development: Bibliometric Analysis and Emerging Research Trends," Sustainability, MDPI, vol. 14(15), pages 1-31, July.
    12. Zongshui Wang & Hong Zhao & Yan Wang, 2015. "Social networks in marketing research 2001–2014: a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 65-82, October.
    13. Yang Li & Yongcheng Qi, 2020. "Asymptotic distribution of modularity in networks," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(4), pages 467-484, May.
    14. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.
    15. Jinyang Dong & Jiamou Liu & Tiezhong Liu, 2021. "The impact of top scientists on the community development of basic research directed by government funding: evidence from program 973 in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8561-8579, October.
    16. Ying Liu & Naizhuo Zhao & Jennifer K Vanos & Guofeng Cao, 2018. "Visualizing changes in nationally averaged PM2.5 concentrations by an alluvial diagram," Environment and Planning A, , vol. 50(2), pages 259-261, March.
    17. Luis Lorenzo & Javier Arroyo, 2022. "Analysis of the cryptocurrency market using different prototype-based clustering techniques," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-46, December.
    18. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    19. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.
    20. Chae, Bongsug (Kevin), 2019. "A General framework for studying the evolution of the digital innovation ecosystem: The case of big data," International Journal of Information Management, Elsevier, vol. 45(C), pages 83-94.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04554013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.