In Social Networks there is a trend for people of similar interests to associate with each other. Such a tendency is called homophily, and in this paper we study the effect it has on calculating user sentiment profiles. We collect Twitter data such as tweets and follower relationships, and we use that to calculate initial user profiles (topic sentiments) and user connectivity. The novel approach in this paper is in the way user sentiment is calculated by using the impact of the related users. This impact related users have on one another depends on the connectivity factor between two people. We show that applying such an approach, which utilizes user connectivity when calculating user profiles, leads to better accuracy and we also show that there are indications it might help in tackling the cold start problem.