The Structural Topic Model and Applied Social Science
Year of Conference
We develop the Structural Topic Model which provides a general way to incorporate corpus structure or document metadata into the standard topic model. Document-level covariates enter the model through a simple generalized linear model framework in the prior distributions controlling either topical prevalence or topical content. We demonstrate the model’s use in two applied problems: the analysis of open-ended responses in a survey experiment about immigration policy, and understanding differing media coverage of China’s rise.
Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation
Lake Tahoe, Nevada
Peer-Reviewed Conference Workshop. Selected for Oral Presentation.