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Concept Mover’s Distance: measuring concept engagement via word embeddings in texts

Author

Listed:
  • Dustin S. Stoltz

    (University of Notre Dame)

  • Marshall A. Taylor

    (New Mexico State University)

Abstract

We propose a method for measuring a text’s engagement with a focal concept using distributional representations of the meaning of words. More specifically, this measure relies on word mover’s distance, which uses word embeddings to determine similarities between two documents. In our approach, which we call Concept Mover’s Distance, a document is measured by the minimum distance the words in the document need to travel to arrive at the position of a “pseudo document” consisting of only words denoting a focal concept. This approach captures the prototypical structure of concepts, is fairly robust to pruning sparse terms as well as variation in text lengths within a corpus, and with pre-trained embeddings, can be used even when terms denoting concepts are absent from corpora and can be applied to bag-of-words datasets. We close by outlining some limitations of the proposed method as well as opportunities for future research.

Suggested Citation

  • Dustin S. Stoltz & Marshall A. Taylor, 2019. "Concept Mover’s Distance: measuring concept engagement via word embeddings in texts," Journal of Computational Social Science, Springer, vol. 2(2), pages 293-313, July.
    • Handle: RePEc:spr:jcsosc:v:2:y:2019:i:2:d:10.1007_s42001-019-00048-6
    DOI: 10.1007/s42001-019-00048-6
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    1. repec:nas:journl:v:115:y:2018:p:e3635-e3644 is not listed on IDEAS
    2. Mark Pagel & Quentin D. Atkinson & Andrew Meade, 2007. "Frequency of word-use predicts rates of lexical evolution throughout Indo-European history," Nature, Nature, vol. 449(7163), pages 717-720, October.
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    Cited by:

    1. Alex Luscombe & Kevin Dick & Kevin Walby, 2022. "Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1023-1044, June.
    2. Taylor, Marshall A. & Stoltz, Dustin S., 2020. "Integrating Semantic Directions with Concept Mover's Distance to Measure Binary Concept Engagement," SocArXiv 36r2d, Center for Open Science.
    3. Batabyal, Amitrajeet & Nijkamp, Peter, 2022. "Introduction to The Creative Class Revisited: New Analytical Advances," MPRA Paper 114163, University Library of Munich, Germany, revised 10 Aug 2022.
    4. Marshall A. Taylor & Dustin S. Stoltz, 2021. "Integrating semantic directions with concept mover’s distance to measure binary concept engagement," Journal of Computational Social Science, Springer, vol. 4(1), pages 231-242, May.
    5. Marozzi, Armando, 2021. "The ECB's tracker: nowcasting the press conferences of the ECB," Working Paper Series 2609, European Central Bank.
    6. Stijn Daenekindt & Julian Schaap, 2022. "Using word embedding models to capture changing media discourses: a study on the role of legitimacy, gender and genre in 24,000 music reviews, 1999–2021," Journal of Computational Social Science, Springer, vol. 5(2), pages 1615-1636, November.
    7. AJ Alvero & Jasmine Pal & Katelyn M. Moussavian, 2022. "Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays," Journal of Computational Social Science, Springer, vol. 5(2), pages 1709-1734, November.

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