Sepsis and septic shock are major concerns in public health as the leading contributors to hospital mortality and cost of treatment in the United States. Early treatment is instrumental for improving patient outcome; to this end, algorithmic methods for early prediction of septic shock have been developed using electronic health record data, with the goal of decreasing treatment delay. We extend a previously-developed method, using a gradient boosting algorithm (XG-Boost) to compute a time-evolving risk of impending transition into septic shock, by combining physiological data from the electronic health record with features obtained from natural language processing of clinical note data. We compare two different methods for generating natural language processing features, with the best method obtaining improved performance of 0.92 AUC, 84% sensitivity, 82% specificity, 49% positive predictive value, and a median early warning time of 7.0 hours. This degree of early warning is sufficient to enable intervention many hours in advance of septic shock onset, with the improved prediction performance of this method resulting in fewer false alarms and thus more actionable predictions.