The Structural Topic Model is a general framework for topic modeling with document-level covariate information. The covariates can improve inference and qualitative interpretability and are allowed to affect topical prevalence, topical content or both. The software package implements the estimation algorithms for the model and also includes tools for every stage of a standard workflow from reading in and processing raw text through making publication quality figures. The workshop will provide a hands-on introduction to using the stm package which includes functionality to ingest and manipulate text data; estimate Structural Topic Models; calculate covariate effects on latent topics with uncertainty; estimate a graph of topic correlations; compute model diagnostics and summary measures; and create the plots used in various papers about stm
Attendees should have previous R experience.
Lecture, discussion and hands-on exercises.
Attendees should bring a laptop with R and the R package stm already installed. The stm package is available on CRAN and can be installed using: install.packages("stm")