Email plays an important role as a medium for the spread of information, ideas, and influence among its users. We present a framework to learn topic-based interactions between pairs of email users, i.e., the extent to which the email topic dynamics of one user are likely to be affected by the others. The proposed framework is built on the influence model and the probabilistic latent semantic analysis (PLSA) language model. This paper makes two contributions. First, we model interactions between email users using the semantic content of email body, instead of email header. Second, our framework models not only email topic dynamics of individual email users, but also the interactions within a group of individuals. Experiments on the Enron email corpus show some interesting results that are potentially useful to discover the hierarchy of the Enron organization. We also present an email visualization and retrieval system which could not only search for relevant emails, but also for the relevant email users.