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A Tool Kit for Relation Induction in Text Analysis

Author

Listed:
  • Dustin S. Stoltz
  • Marshall A. Taylor
  • Jennifer S. K. Dudley

Abstract

Distances derived from word embeddings can measure a range of gradational relations—similarity, hierarchy, entailment, and stereotype—and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation induction, and demonstrate how such techniques can complement dictionary methods as unsupervised, deductive methods.

Suggested Citation

  • Dustin S. Stoltz & Marshall A. Taylor & Jennifer S. K. Dudley, 2025. "A Tool Kit for Relation Induction in Text Analysis," Sociological Methods & Research, , vol. 54(2), pages 565-604, May.
    • Handle: RePEc:sae:somere:v:54:y:2025:i:2:p:565-604
    DOI: 10.1177/00491241241233242
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    References listed on IDEAS

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    1. Paris Aslanidis, 2018. "Measuring populist discourse with semantic text analysis: an application on grassroots populist mobilization," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(3), pages 1241-1263, May.
    2. Rodman, Emma, 2020. "A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors," Political Analysis, Cambridge University Press, vol. 28(1), pages 87-111, January.
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    1. Anggraini, Puspita Ghaniy & Sholihin, Mahfud & Mangena, Musa & Wijayana, Singgih & Rakhman, Fu’ad, 2025. "The determinants and consequences of tax avoidance: A psychological contract perspective," Research in International Business and Finance, Elsevier, vol. 79(C).

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