Social networks are an important part of the everyday activities of a big part of people. Different type of socialbased activities (e.g. online product shopping, question answering forums and etc.) create a vast connection between users. One of the most important features of these networks is knowledge sharing. This knowledge usually provides better insight for the users and consequently has a direct impact on the decision made by them. For example, online shopping members usually take their decision based on this shared information. But the main issue is there are a huge amount of shared knowledge without an accurate mechanism to determine their validity. One approach is to count more on the influential users opinions in the system and toward this end, several ranking algorithms have been proposed. But the existing algorithms for users ranking don't consider the personality features of users in their methodology. In this paper, we use this new feature of personality in the ranking algorithm for influential user detection in signed networks. We used Optimism and Pessimism scores as personality features of each user and employ it in the PageRank algorithm as a sample ranking algorithm and evaluated the new ranking results by using a new metric of credibility. To assess the performance of the proposed method, we applied it to a large dataset of Epinions signed networks. The results are compared with state-of-the-art expert finding algorithms which indicate that the personality feature can effectively improve the ranking and influential user detection accuracy