Capturing distinctions while mining text data: Toward low-tech formalization for text analysis

Ronald L. Breiger, Robin Wagner-Pacifici, John W. Mohr

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


In this article we consider some low-tech approaches to text mining. Our goal is to articulate a RiCH (Reader in Control of Hermeneutics) style of text analysis that takes advantage of the digital affordances of modern reading practices and easily deployable computational tools while also preserving the primacy of the interpretive lens of the human reader. In the article we offer three analytical interventions that are suitable to the low-tech formalizations we propose: the first and most developed intervention tracks the (normally computationally ignored) “stop” words; the second identifies the use of strategic anxiety terms in the texts; and the third (less developed in this article) introduces the grammatical features of modality (including modalization statements of probability and usuality, and modulation statements regarding degrees of obligation and inclination). All three analytical interventions provide a productive tracking of various modes and degrees of strategic decisiveness, contradiction, uncertainty and indeterminacy in a corpus of recent U.S. National Security Strategy reports.

Original languageEnglish (US)
Pages (from-to)104-119
Number of pages16
StatePublished - Jun 2018


  • Big data
  • Close reading
  • Computational sociology
  • Hermeneutics
  • National security
  • Text mining

ASJC Scopus subject areas

  • Cultural Studies
  • Language and Linguistics
  • Communication
  • Sociology and Political Science
  • Linguistics and Language
  • Literature and Literary Theory


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