Concept Class Analysis: A Method for Identifying Cultural Schemas in Texts

Marshall A. Taylor, Dustin S. Stoltz

Sociological Science November 9, 2020
10.15195/v7.a23


Recent methodological work at the intersection of culture, cognition, and computational methods has drawn attention to how cultural schemas can be “recovered” from social survey data. Defining cultural schemas as slowly learned, implicit, and unevenly distributed relational memory structures, researchers show how schemas—or rather, the downstream consequences of people drawing upon them—can be operationalized and measured from domain-specific survey modules. Respondents can then be sorted into “classes” on the basis of the schema to which their survey response patterns best align. In this article, we extend this “schematic class analysis” method to text data. We introduce concept class analysis (CoCA): a hybrid model that combines word embeddings and correlational class analysis to group documents across a corpus by the similarity of schemas recovered from them. We introduce the CoCA model, illustrate its validity and utility using simulations, and conclude with considerations for future research and applications.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Marshall A. Taylor: Department of Sociology, New Mexico State University
E-mail: mtaylor2@nmsu.edu

Dustin S. Stoltz: Department of Sociology and Anthropology, Lehigh University
E-mail: dss219@lehigh.edu

Acknowledgments: A replication repository for this article can be found at: https://github.com/Marshall-Soc/CoCA. We thank Jesper Sørensen, the deputy editor, and the consulting editor for their thoughtful comments on this article.

  • Citation: Taylor, Marshall A., and Dustin S. Stoltz. 2020. “Concept Class Analysis: A Method for Identifying Cultural Schemas in Texts.” Sociological Science 7:544-569.
  • Received: July 31, 2020
  • Accepted: October 4, 2020
  • Editors: Jesper Sørensen, Gabriel Rossman
  • DOI: 10.15195/v7.a23


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