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Structural Topic Models for Open‐Ended Survey Responses
Author(s) -
Roberts Margaret E.,
Stewart Brandon M.,
Tingley Dustin,
Lucas Christopher,
LederLuis Jetson,
Gadarian Shana Kushner,
Albertson Bethany,
Rand David G.
Publication year - 2014
Publication title -
american journal of political science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.347
H-Index - 170
eISSN - 1540-5907
pISSN - 0092-5853
DOI - 10.1111/ajps.12103
Subject(s) - computer science , data science , coding (social sciences) , information retrieval , politics , psychology , sociology , social science , political science , law
Collection and especially analysis of open‐ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open‐ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.