Tweets exchanged over the Internet are an important source of information even if their characteristics make them dif- ficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we address the problem of extracting relevant topics through tweets coming from different commu- nities. More precisely we are interested to address the fol- lowing question: which are the most relevant terms given a community. To answer this question we define and evaluate new variants of the traditional TF-IDF. Furthermore we also show that our measures are well suited to recommend a community affiliation to a new user. Experiments have been conducted on tweets collected during French Presiden- tial and Legislative elections in 2012. The results underline the quality and the usefulness of our proposal.