We introduce a joint model of network content and context designed for exploratory analysis of email networks via visualization of topic-specific communication patterns. Our model is based on a novel extension of the latent space network model to the mixed-membership framework, and it uses latent Dirichlet allocation to model the text attributes of our data. To perform inference in this model, we use an approximate stochastic expectation-maximization algorithm. We validate the appropriateness of our model using a simulation study and a prediction task, and demonstrate its capabilities by investigating the communication patterns within a new government email dataset, the New Hanover County email corpus.