Topic Modeling: Latent Semantic Analysis for the Social Sciences

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 over...

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Bibliographic Details
Published in:Social science quarterly 2018-11, Vol.99 (5), p.1665-1679
Main Authors: Valdez, Danny, Pickett, Andrew C., Goodson, Patricia
Format: Article
Language:English
Subjects:
Candidates
Consciousness
Data analysis
Data mining
Internet
Semantic analysis
Semantics
Social research
Social sciences
Topics
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Summary: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.
ISSN:0038-4941
1540-6237
DOI:10.1111/ssqu.12528