With the development and increasing impact of social media (e.g. microblog), it is critical to analyze the topic of the microblog. Topic modeling can extract topics from text data. However, it is a challenging task on the microblog data, due to the short content, heavy noises and limited amount of information in each microblog message. This article proposes a visual analytics system for microblog topic modeling. The proposed system enables the visual exploration and analysis process of the topic modeling results of microblogs with multiple linked views and interactions. It considers user behaviors and time effects in the topic modeling process. Users can analyze topics of microblog from multiple perspectives. The system also supports interactive topic editing to improve the topic modeling results in accuracy and reliability. The case study confirms that the described system can effectively help users analyze the Sina Weibo contents interactively.