Hashtags are crucial social data that are useful for multiple purposes, such as tweets' classification, indexing, content categorization, search or recommendation. Hashtags recommendation on Twitter is an important issue, seeing the importance of this social data and the need of social users to be provided with personalized information that fits their interests. Users recommendation is also an eminent task as it allows a user to broaden his network by users with similar interests. This work focuses on how hashtags can be analyzed for recommendation purposes. First, we present a state of the art reviewing social analysis, users and hashtags' recommendation. We then present a new architecture of hashtags and users recommender system based on hashtags' semantic analysis. This proposal is based on ontology as a semantic resource. We build social user profiles, analyze hashtags and study their contextual and temporal cooccurrence. We then propose two novel ranking schemes. The first is HF-IUTF scheme that weights hashtags in the user profile. We use it to rank and filter the representative hashtags of each user's social profile. The second contribution is HF-IGHF scheme: Given a group of users, this method identifies the hashtags representative of the group. We apply spectral clustering algorithm to obtain similar clusters. We can then recommend similar users of the same cluster and representative new and recent hashtags of users in the cluster. The originality of this architecture is the way user profiles will be semantically indexed and analyzed, and ranking schemes that are independent of the way the similarity group is built.