Influence maximization in signed social networks is one of the influence maximization problems that involve competitive positive influence and negative influence. Most works focus on either positive influence maximization or negative influence blocking problems. Some works take both positive influence and negative influence into consideration, but their proposed methods take a lot of time. We proposed a time-saving framework, Edge-view Signed Influence Maximization (ESIM). ESIM estimates the power of influence flow on edges to make a balance between positive influence maximization and negative influence blocking. The experiment shows that our framework is not only efficient but also effective on real world signed social networks.