A key role in OLAP analyses of textual user-generated content for social business intelligence (SBI) is played by topics, i.e., concepts of interest within a subject area. Topic hierarchies are irregular, heterogeneous, dynamic, and possibly schemaless; besides, unlike in traditional OLAP, different semantics for topic aggregation can be envisioned. In this demonstration we present an architecture for SBI based on meta-stars, a novel approach to topic modeling in ROLAP systems. By coupling meta-modeling with navigation tables, meta-stars can cope with changes in the schema of irregular hierarchies and with schemaless ones; besides, they enable a new class of OLAP queries based on semantically-aware aggregation. The demonstration will focus both on the hierarchy update process and on the querying expressiveness.