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A Computational Approach to Qualitative Analysis in Large Textual Datasets

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  • Michael S Evans

Abstract

In this paper I introduce computational techniques to extend qualitative analysis into the study of large textual datasets. I demonstrate these techniques by using probabilistic topic modeling to analyze a broad sample of 14,952 documents published in major American newspapers from 1980 through 2012. I show how computational data mining techniques can identify and evaluate the significance of qualitatively distinct subjects of discussion across a wide range of public discourse. I also show how examining large textual datasets with computational methods can overcome methodological limitations of conventional qualitative methods, such as how to measure the impact of particular cases on broader discourse, how to validate substantive inferences from small samples of textual data, and how to determine if identified cases are part of a consistent temporal pattern.

Suggested Citation

  • Michael S Evans, 2014. "A Computational Approach to Qualitative Analysis in Large Textual Datasets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
  • Handle: RePEc:plo:pone00:0087908
    DOI: 10.1371/journal.pone.0087908
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    References listed on IDEAS

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    1. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    2. Sally Eden & Andrew Donaldson & Gordon Walker, 2006. "Green Groups and Grey Areas: Scientific Boundary-Work, Nongovernmental Organisations, and Environmental Knowledge," Environment and Planning A, , vol. 38(6), pages 1061-1076, June.
    3. Justin Grimmer, 2013. "Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation," American Journal of Political Science, John Wiley & Sons, vol. 57(3), pages 624-642, July.
    4. David J. Newman & Sharon Block, 2006. "Probabilistic topic decomposition of an eighteenth‐century American newspaper," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(6), pages 753-767, April.
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    Cited by:

    1. Ariane Tichit & Clément Mathonnat & Diego Landivar, 2016. "Classifying Non-Bank Currency Systems Using Web Data," Post-Print hal-01995950, HAL.
    2. Diego Sébastien LANDIVAR & Clément MATHONNAT & Ariane TICHIT, 2014. "Classification des systèmes de monnaies non-bancaires : ce que disent les données du Web," Working Papers 201425, CERDI.
    3. Ariane TICHIT & Clément MATHONNAT & Diego Sébastien LANDIVAR, 2015. "Classifying Non-banking Monetary Systems using Web Data," Working Papers 201530, CERDI.

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