We consider the Unconstrained Submodular Maximization problem in which we are given a nonnegative submodular function f : 2(N) -> R+, and the objective is to find a subset S subset of N maximizing f(S). This is one of the most basic submodular optimization problems, having a wide range of applications. Some well-known problems captured by Unconstrained Submodular Maximization include Max-Cut, Max-DiCut, and variants of Max-SAT and maximum facility location. We present a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige, Mirrokni, and Vondrak [SIAM J. Comput., 40 (2011), pp. 1133-1153]. Our algorithm is based on an adaptation of the greedy approach which exploits certain symmetry properties of the problem.