Many real-world networks have signed relationships between the nodes. Identification of these relationships is an important aspect of decision making. The existing signed relationships in a network may impact the relationships between the other nodes, hence learning from the existing signed relationships in a network can be used for decision making in various mining tasks. These signed networks are getting attention in recent years due to their relevance to many applications such as categorization, recommendation, and relationship discovery in various domains for decision support such as biological, social network analysis, communication and making knowledge graphs. In this work, we focus on edge classification (sign/label prediction for edges) in unweighted and undirected signed networks where the task is to predict the label of the unlabeled edges. Edge classification is a challenging problem as in real-world signed networks, edges are scarcely labeled. In our work, we are using labeled edges to predict the sign of unlabeled edges (classification) with the help of structural information. In this work, we have proposed a novel framework named NPECF for the classification of unlabeled edges. The proposed framework is novel in its way of utilizing the existing information in the signed network to predict the label of unlabeled edges. The utilization of the unlabeled edges in NPECF using three spanning subgraph projections of the given network minimizes the information loss. The experiments have been performed on four real-world datasets from different domains to demonstrate the effectiveness of the proposed framework.