In social media, such as blogs, since the content naturally evolves over time, it is hard or in many cases impossible to organize the content for effective navigation. Thus, one commonly has to resort to simple tools, such as tags and tag clouds, for presenting frequently used keywords to users to provide them at least some high level idea about the content of a given set of social media entries. Most visualizations of tag clouds vary the sizes of the fonts to differentiate important tags from those that are less important. We propose an alternative "contextual-layout" method, TMine, for analyzing and presenting tags that are extracted from textual content. In TMine tags are first mapped onto a latent semantic space. Then, TMine analyzes the relationships between tags relying on an extended boolean interpretation of the semantic space. The tag cloud is condensed into a hierarchy in a way that captures contextual relationships between tags: in particular, descendant terms in the hierarchy occur within the context defined by the ancestor terms. This provides a mechanism for navigation within the tag space as well as for classification of the text documents based on the contextual structure implied by the tags.