@article{33311,
  author = {Margaret Roberts and Brandon Stewart and Dustin Tingley and Christopher Lucas and Jetson Leder-Luis and Shana Gadarian and Bethany Albertson and David Rand},
  title = {Structural topic models for open-ended survey responses},
  abstract = { <p>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, semi-automated 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{\textquoteright}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.</p> },
  year = {2014},
  journal = {American Journal of Political Science},
  volume = {58},
  pages = {1064-1082},
  language = {eng},
}