The ever rising amount of business communications results in a growing amount of qualitative data relevant to many decision situations. This increase in information volume and velocity threatens to overburden decision makers. We provide a structured approach towards this problem using topic-models to reduce information overload by filtering content and by providing context-relevant information to decision makers. Building upon theoretical considerations related to phases of the decision process established by Herbert A. Simon, we implement the proposed approach on the example of a large document collection of stock analyst reports and analyst conference calls using Latent Dirichlet Allocation (a topic model). Thereby, we extract investment-relevant topics from the model and discuss the opportunities for decision support resulting from the chosen approach.