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The workforce analyzer: group discovery among LinkedIn public profiles

  • Kais Dai
  • Ana Fernández Vilas
  • Rebeca P. Díaz Redondo
Original Research

Abstract

In this paper, we describe two users’ group discovery methods among LinkedIn public profiles. We start by clustering profiles according to their professional background. In this sense, we combine the so-called K-means technique with the gap statistics method and use tag clouds to scrutinize the obtained groups. The second phase of this work consists in classifying the same profiles by relying on a knowledge base. In this context, we design a support-vector-machines multi-label classifier that takes advantage of the LinkedIn job Ads taxonomy. We finally contrast results of both methods and provide insights about the trending professional orientations of the workforce from an online perspective.

Keywords

Group discovery LinkedIn Text mining Clustering Multi-label classification N-Gram 

Notes

Acknowledgements

This work is funded by Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R); the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC). This work is also partially funded by the European Commission under the Erasmus Mundus GreenIT Project (3772227-1-2012-ES-ERA MUNDUS-EMA21).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Kais Dai
    • 1
  • Ana Fernández Vilas
    • 1
  • Rebeca P. Díaz Redondo
    • 1
  1. 1.Information and Computing Laboratory, AtlantTIC Research CenterUniversity of VigoVigoSpain

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