Datenbank-Spektrum

, Volume 15, Issue 1, pp 7–14

Content Analysis between Quality and Quantity

Fulfilling Blended-Reading Requirements for the Social Sciences with a Scalable Text Mining Infrastructure
  • Matthias Lemke
  • Andreas Niekler
  • Gary S. Schaal
  • Gregor Wiedemann
Schwerpunktbeitrag

DOI: 10.1007/s13222-014-0174-x

Cite this article as:
Lemke, M., Niekler, A., Schaal, G. et al. Datenbank Spektrum (2015) 15: 7. doi:10.1007/s13222-014-0174-x

Abstract

Social science research using Text Mining tools requires—due to the lack of a canonical heuristics in the digital humanities—a blended reading approach. Integrating quantitative and qualitative analyses of complex textual data progressively, blended reading brings up various requirements for the implementation of Text Mining infrastructures. The article presents the Leipzig Corpus Miner (LCM), developed in the joint research project ePol—Post-Democracy and Neoliberalism and responding to social science research requirements. The functionalities offered by the LCM may serve as best practice of processing data in accordance with blended reading.

Keywords

Text Mining Qualitative Analysis Blended Reading Mixed Methods Corpus Linguistics 

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Matthias Lemke
    • 1
  • Andreas Niekler
    • 2
  • Gary S. Schaal
    • 1
  • Gregor Wiedemann
    • 2
  1. 1.Institute for Political Science, specialization on Political TheoryHelmut-Schmidt-University Hamburg (HSU/UniBw H)HamburgGermany
  2. 2.Natural Language Processing Group, Department of Computer ScienceUniversity of LeipzigLeipzigGermany