Existing works on influence maximization mainly focus on unsigned network and neglect the hostile relationship between the individuals in the network. Aiming at the positive influence maximization problem in signed network， based on Polarity-related Independent Cascade （IC-P） model， a Reverse Influence Sampling in Signed network （RIS-S） algorithm was proposed to maximize positive influence. Firstly， in order to apply to the signed network， the polarity relationships of nodes in the stage of generating reverse reachable sets were considered. Secondly， to improve the effectiveness of reverse reachable sets， the traversal depth of sampling was limited. Finally， the positive influence ranges and running times of RIS-S， Influence Maximization via Martingales （IMM）， Positive Out-Degree （POD） and Effective Degree algorithm were compared on three real signed network data sets to verify the effectiveness of the proposed algorithm. Experimental results show that RIS-S algorithm can obtain wider positive influence range by selecting more accurate seeds， and the proposed algorithm has the running time less than the same type algorithm IMM.It can be thought that RIS-S algorithm can solve the problem of positive influence maximization in signed network.