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Topic Modeling: Latent Semantic Analysis for the Social Sciences

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  • Danny Valdez
  • Andrew C. Pickett
  • Patricia Goodson

Abstract

Objective Topic modeling (TM) refers to a group of methods for mathematically identifying latent topics in large corpora of data. Although TM shows promise as a tool for social science research, most researchers lack awareness of the tool's utility. Therefore, this article provides a brief overview of TM's logic and processes, offers a simple example, and suggests several possible uses in social sciences. Methods Using latent semantic analysis in our example, we analyzed transcripts of the 2016 U.S. presidential debates between Hillary Clinton and Donald Trump. Results Resulting topics paralleled the most frequent policy‐related Internet searches at the time. When divided by candidate, changes in emergent topics reflected individual policy stances, with nuanced differences between the two. Conclusion Findings underscored the utility of TM to identify thematic patterns embedded in large quantities of text. TM, therefore, represents a valuable addition to the social scientist's methodological tool set.

Suggested Citation

  • Danny Valdez & Andrew C. Pickett & Patricia Goodson, 2018. "Topic Modeling: Latent Semantic Analysis for the Social Sciences," Social Science Quarterly, Southwestern Social Science Association, vol. 99(5), pages 1665-1679, November.
    • Handle: RePEc:bla:socsci:v:99:y:2018:i:5:p:1665-1679
    DOI: 10.1111/ssqu.12528
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    Cited by:

    1. Kataishi, Rodrigo & Brixner, Cristian & Calá, Carla Daniela & Niembro, Andrés, 2023. "Crisis, resiliencia e innovación en sectores estratégicos: reconfiguraciones en el complejo turístico de Tierra del Fuego," Nülan. Deposited Documents 4000, Universidad Nacional de Mar del Plata, Facultad de Ciencias Económicas y Sociales, Centro de Documentación.
    2. Simona Fiandrino & Alberto Tonelli, 2021. "A Text-Mining Analysis on the Review of the Non-Financial Reporting Directive: Bringing Value Creation for Stakeholders into Accounting," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    3. Hoang, Yen Hai & Ngo, Vu Minh & Bich Vu, Ngoc, 2023. "Central bank digital currency: A systematic literature review using text mining approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Gianluca Stefani & Giuseppe Nocella & Giovanna Sacchi, 2020. "Piloting a Meta-Database of Agroecological Transitions: An Example from Sustainable Cereal Food Systems," Agriculture, MDPI, vol. 10(6), pages 1-14, June.

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