We provide an overview on the development of a fine-grained, aspect-based sentiment analysis approach aimed at providing useful signals to improve forecasts of economic models and produce more accurate predictions. The approach is unsupervised since it relies on external lexical resources to associate a polarity score to a given term or concept. After providing an overview of the method under development, some preliminary findings are also given.