Topic Modeling: Latent Semantic Analysis for the Social Sciences

Danny Valdez, Andrew C. Pickett and Patricia Goodson

Social Science Quarterly, 2018, vol. 99, issue 5, 1665-1679

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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1111/ssqu.12528

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:socsci:v:99:y:2018:i:5:p:1665-1679

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0038-4941

Access Statistics for this article

Social Science Quarterly is currently edited by Robert L. Lineberry

More articles in Social Science Quarterly from Southwestern Social Science Association
Bibliographic data for series maintained by Wiley Content Delivery ().