Abstract: A system for providing a multi-technology visual environment for management of issues. A Topic Model is trained via a text corpus for each issue via Topic Model Trainer, also known as the Topic Modeler. Then, incoming discourse texts (such as tweets) are classified to issues organizations care about and are thus relevant, or classified as irrelevant via a Classification Module. Finally, the resulting list of relevant discourse surrounding the issues is scored via a Scoring Module. The modules are configured accordingly. A topic training module configured to allow an user to specify a text corpus representing the discourse surrounding an issue. A topic classification module configured to analyze the data to remove non-material information and classify the text discourse data based on topic. A scoring module configured to assign numerical scores to the classified discourse text based on the issue it is about and the type of entity that created the discourse text for an identified period of time.