Learning low-dimensional node representations is crucial for analysis in signed networks. Existing embedding methods, often based on local structural balance theory, tend to overlook network-wide information. This is a critical omission, as negative links can create globally pivotal nodes—such as those bridging conflicting communities—whose importance cannot be captured by local analysis alone. To address this limitation, we propose SAGA (Signed-Aware Global Attention), a novel signed network embedding framework. SAGA first utilizes a signed graph neural network to learn local representations that differentiate between positive and negative ties. It then introduces a global pooling mechanism that generates a graph-level summary, enabling the model to generate node embeddings that reflect both their global context and structural significance. Experiments on five real-world datasets demonstrate that SAGA consistently outperforms existing methods on downstream tasks, validating its effectiveness in capturing both local and global network properties.