This paper proposes a new Direction Aware Particle Swarm Optimization algorithm with Sensitive Swarm Leader (DAPSO-SSL). DAPSO-SSL maps the basic human nature of awareness, maturity, leader and followers relationship and leadership qualities to the popular PSO algorithm. It assigns these qualities to swarm leader and individual particles. In practical life, it is the moral responsibility of the leader to improve the status, quality or direction of the life of his followers. Thus, he influences the decision making of the group members through his policies and actions. A great leader is one which continuously keeps track of his followers' performance and accordingly adapts to various situations. If their performance is degrading because of him, he can either change his policies or can groom a new leader to take his position. Hence this leader can be Sensitive to the needs of group members. It also incorporates the concept of iterative directional awareness among the swarm particles in PSO. The particles over successive iterations become more conscious about their direction of motion by taking account of their performances. DAPSO-SSL thus tries to prevent stagnation while improving convergence rate. The algorithm is tested on twenty-four benchmark functions on COCO framework and its performance is compared with other state-of-the-art algorithms. Further, in order to check the effectiveness of the proposed algorithm, DAPSO-SSL is applied to community detection problem of big data networks. The comparative analysis of the results with other state-of-the-art algorithms has indicated the competitiveness of the proposed algorithm.