Social media have become an important source of data and can provide near-instantaneous information which can be analysed to generate predictive models and to support decision making. Much work has been done in short message analysis such as trend analysis, short message classification, etc. However, to generate an accurate and concise conclusion/assertion from all the relevant information remains challenging. In this paper we propose a method to analyse microblog messages at both ‘word/term’ level and ‘concept’ level to generate assertions accurately and instantly. To analyse the concept level, we define a small seed ontology which is a semi-automatically generated extension of an existing ontology. By doing this we achieve both accurate assertions and avoid the costly overhead of defining the whole knowledgebase manually. We then use the proposed method to make traffic assertions from a microblog stream to demonstrate the advantages of the approach.