1. A method for data clustering and user modeling for next-best-action decisions, the method comprising the computer-implemented steps of: receiving unstructured social data of a plurality of users, the unstructured social data comprising one or more indicators including a set of words located in the unstructured social data that indicate at least one of: sentiment, personality, and emotional state; analyzing the unstructured social data of each user of the plurality of users to assign a numerical value to each of a plurality of feature vectors to associate with each of the plurality of users based on the set of words of the one or more indicators located in the unstructured social data generated by the user, each of the set of feature vectors corresponding to one or more personality characteristics that include a learning style, a propensity to purchase, a socioeconomic class, and a personality trait of each of the plurality of users; analyzing the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors; grouping the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identifying attributes of the cluster based on the feature vectors of the users grouped in the cluster; and inputting the attributes of the cluster into a predictive model to automatically determine a commercial offer that corresponds to the cluster.