This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies on a user profile represented as a set of weighted concepts related to the user’s interests. The weighting function, we named sentiment-volume-objectivity (SVO) function, takes into account not only the user’s sentiment toward his/her interests, but also the volume and objectivity of related contents. A clustering technique based on modularity optimization enables us to identify the latent sentiment communities. A preliminary experimental evaluation on real-world datasets from Twitter shows the benefits of the proposed approach and allows us to make some considerations about the detected communities.