Tags are intensively used in social platforms to annotate resources: Tagging is a social phenomenon, because users do not only annotate to organize their resources but also to associate semantics to resources contributed by third parties. This leads often to semantic ambiguities: Popular tags are associated with very disparate meanings, even to the extend that some tags (e.g. "beautiful" or "toread") are irrelevant to the semantics of the resources they annotate. We propose a method that learns a topic model for documents under a tag and visualizes the different meanings associated with the tag. Our approach deals with the following problems. First, tag miscellany is a temporal phenomenon: tags acquire multiple semantics gradually, as users apply them to disparate documents. Hence, our method must capture and visualize the evolution of the topics in a stream of documents. Second, the meanings associated to a tag must be presented in a human-understandable way; This concerns both the choice of words and the visualization of all meanings. Our method uses AdaptivePLSA, a variation of Probabilistic Latent Semantic Analysis for streams, to learn and adapt topics on a stream of documents annotated with a specific tag. We propose a visualization technique called Topic Table to visualize document prototypes derived from topics and their evolution over time. We show by a case study how our method captures the evolution of tags selected as frequent and ambiguous, and visualizes their semantics in a comprehensible way. Additionally, we show the effectiveness by adding alien resources under a tag. Our approach indeed visualizes hints to the added documents.