Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of ''us-versus-them'' that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We first propose the Signed Latent Distance Model (SLDM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks while extending the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes, forming the Signed Latent relational dIstance Model (SLIM). On four real social signed networks of polarization, we demonstrate that the models extract low-dimensional characterizations that well predict friendships and animosity while SLIM provides interpretable visualizations defined by extreme positions when endowing the model with an embedding space restricted to polytopes.