This project interprets free text in a large dataset from the domain of depression using sentiment analysis techniques. It validates an existing predictive computational model for depression using the (interpreted) dataset, and tries to generate enhancements to the computational model by applying learning techniques upon the dataset (more in specific, Genetic Programming).