This paper studies a method for identifying word unigrams and word big rams that are associated with one or more human values such as freedom or innovation. The key idea is to deterministically associate values with word choices, thus permitting values reflected by sentences to be assigned using dictionary lookup. This approach works nearly as well on average as the most accurate existing methods, and at close to the best results that can be achieved by a second human annotator, but the principal contribution of the new method is that the basis for the system's classification decisions are more easily interpreted by social scientists. The new method is based using a Monte Carlo algorithm with simulated annealing to efficiently explore the space for optimal assignments of human values to unigrams and big rams. Results are reported on an annotated test collection of prepared statements from witnesses at public hearings on the topic of net neutrality. The results include accuracy comparisons with the previously reported approach.