  Social scientists have recently turned to analyzing text using tools from
natural language processing like word embeddings to measure concepts like
ideology, bias, and affinity. However, word embeddings are difficult to use in
the regression framework familiar to social scientists: embeddings are are
neither identified, nor directly interpretable. I offer two advances on
standard embedding models to remedy these problems. First, I develop Bayesian
Word Embeddings with Automatic Relevance Determination priors, relaxing the
assumption that all embedding dimensions have equal weight. Second, I apply
work identifying latent variable models to anchor the dimensions of the
resulting embeddings, identifying them, and making them interpretable and
usable in a regression. I then apply this model and anchoring approach to two
cases, the shift in internationalist rhetoric in the American presidents'
inaugural addresses, and the relationship between bellicosity in American
foreign policy decision-makers' deliberations. I find that inaugural addresses
became less internationalist after 1945, which goes against the conventional
wisdom, and that an increase in bellicosity is associated with an increase in
hostile actions by the United States, showing that elite deliberations are not
cheap talk, and helping confirm the validity of the model.
