Modeling and predicting political opinions remains a challenging task for AI systems. We introduce PoliSpace, a framework that embeds individuals and political questions into a shared latent space, where vector alignment directly reflects agreement. Individuals are represented as learnable vectors, and questions are encoded via a contrastively fine-tuned language model with individual-specific affine transformations to capture subjective interpretation. This design yields an embedding space that is robust to question rephrasing and sensitive to negation. Evaluated on real-world voting data from Swiss national elections, PoliSpace achieves an 89.81% F1 score on seen opinion prediction for rephrased questions, outperforming prior embedding-based approaches (66.17% F1). We further introduce retrieval and confidence-aware thresholding mechanisms that reach up to 100% F1 at reduced coverage, demonstrating how the learned embedding space can be leveraged to improve prediction reliability. Ablation studies confirm the importance of both individual-specific interpretation modeling and negation-aware encoder fine-tuning. The learned individual embedding space also exhibits clear clustering by political party affiliation, despite no party-label supervision during training, capturing the underlying ideological structure of the Swiss political landscape.