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Content Analysis between Quality and Quantity

Fulfilling Blended-Reading Requirements for the Social Sciences with a Scalable Text Mining Infrastructure

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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.

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Notes

  1. http://www.epol-projekt.de; for the heuristic interest articulated by the Political Science branch of the project, see Lemke and Schaal [16: 3–19].

  2. http://lisd.princeton.edu/projects/diachronic-global-corpus-digcor

  3. http://translantis.wp.hum.uu.nl

  4. Wiedemann et al. [27: pp. 101 ff].

  5. See http://atlasti.com

  6. Currently we are trying to optimize classification results, before we run final classifications for different sub collections. For now we achieve F1 = 0.613 and accuracy = 0.867 on our category of neoliberal argumentation (interrater reliability during manual annotation phase: Krippendorf’s alpha = 0.76).

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Acknowledgements

ePol is a joint research project of the Institute for Political Science, specialization on Political Theory at Helmut-Schmidt-University Hamburg (Prof. Dr. Gary Schaal) and the Natural Language Processing Group, Department of Computer Science, University of Leipzig (Prof. Dr. Gerhard Heyer). The project is funded by the Federal ministry of education and research (BMBF; FKZ 01UG1231A and B).

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Lemke, M., Niekler, A., Schaal, G. et al. Content Analysis between Quality and Quantity. Datenbank Spektrum 15, 7–14 (2015). https://doi.org/10.1007/s13222-014-0174-x

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