Bipolar soft topological spaces are mathematical expressions to estimate interpretation of data frameworks. Bipolar soft theory considers the core features of data granules. Bipolarity is important to distinguish between positive information which is guaranteed to be possible and negative information which is forbidden or surely false. Connectedness and compactness are the most important fundamental topological properties. These properties highlight the main features of topological spaces and distinguish one topology from another. Taking this into account, we explore the bipolar soft connectedness, bipolar soft disconnectedness and bipolar soft compactness properties for bipolar soft topological spaces. Moreover, we introduce the notion of bipolar soft disjoint sets, bipolar soft separation, and bipolar soft hereditary property and study on bipolar soft connected and disconnected spaces. By giving the detailed picture of bipolar soft connected and disconnected spaces we investigate bipolar soft compact spaces and derive some results related to this concept.
