To Scrap the LinkedIn Data to Create the Organization’s Team Chart

  • Sandeep MathurEmail author
  • Shally Sharma
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


From past decades, LinkedIn appears as the professional connection site for both the freelancers and the recruiters. LinkedIn users are ought to post jobs, connect different industries and updates the people with current events. The goal of the paper is to create a report on the structure of the Organization to provide a smooth and efficient reporting hierarchy which involves data Analytics on the LinkedIn Data. So it is required to create an organizational hierarchy. Web Scraping is performed on the LinkedIn site for this intended purpose.


Data analysis Component Formatting Style Styling 


  1. 1.
    Glez, D., et al.: Web scraping technologies in API world. Brief. Bioinform. 15(5), 788–797 (2014)CrossRefGoogle Scholar
  2. 2.
    Vargiu, E., Urru, M.: Exploiting web scraping in a collaborative filtering based approach to web advertising. Artif. Intell. Res. 2(1), 44–54 (2013)Google Scholar
  3. 3.
    Cowan, G.: Statistical Data Analysis. Oxford university press, Oxford (1998)Google Scholar
  4. 4.
    Mitchell, R.: Web Scraping with PythonGoogle Scholar
  5. 5.
    Armano, G., Vargiu, E.: A unifying view of contextual advertising and recommender systems. In: Proceedings of International Conference on Knowledge Discovery and Information Retrieval, pp. 463–466 (2010)Google Scholar
  6. 6.
    Koolen, M., Kamps, J.: Are semantically related links more effective for retrieval? In: Proceedings of the 33rd European Conference on Advances in Information Retrieval, pp. 92–103. Springer, Heidelberg (2011)Google Scholar
  7. 7.
    Anagnostopoulos, A., Broder, A.Z., Gabrilovich, E., Josifovski, V., Riedel, L.: Just-in-time contextual advertising. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 331–340. ACM, New York (2007)Google Scholar
  8. 8.
    Lacerda, A., Cristo, M., Gonçalves, M.A., Fan, W., Ziviani, N., Ribeiro-Neto, B.: Learning to advertise. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549–556. ACM, New York (2006)Google Scholar
  9. 9.
  10. 10.
  11. 11.
    Berry, M.W.: Survey of Text Mining. Springer, New York (2003)Google Scholar
  12. 12.
    Adams, A., McCrindle, R.: Pandora’s Box: Social and Professional Issues of the Information Age. Wiley, Hoboken (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Amity Institute of Information TechnologyAmity UniversityNoidaIndia

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