  This paper contributes to an emerging literature that models votes and text
in tandem to better understand polarization of expressed preferences. It
introduces a new approach to estimate preference polarization in
multidimensional settings, such as international relations, based on
developments in the natural language processing and network science literatures
-- namely word embeddings, which retain valuable syntactical qualities of human
language, and community detection in multilayer networks, which locates densely
connected actors across multiple, complex networks. We find that the employment
of these tools in tandem helps to better estimate states' foreign policy
preferences expressed in UN votes and speeches beyond that permitted by votes
alone. The utility of these located affinity blocs is demonstrated through an
application to conflict onset in International Relations, though these tools
will be of interest to all scholars faced with the measurement of preferences
and polarization in multidimensional settings.
