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Social media monitoring has become an important means forbusiness analytics and trend detection, for instance, analyzing the senti-ment towards a certain product or decision. While a lot of work has beendedicated to analyze sentiment for English texts, much less effort hasbeen put into providing accurate sentiment classification for the Germanlanguage. In this paper, we analyze three established classifiers for theGerman language with respect to Facebook posts. We then present ourown hierarchical approach to classify sentiment and evaluate it using adata set of∼640 Facebook posts from corporate as well as governmentalFacebook pages. We compare our approach to three sentiment classifiersfor German, i.e. AlchemyAPI, Semantria and SentiStrength. With anaccuracy of 70 %, our approach performs better than the other classi-fiers. In an application scenario, we demonstrate our classifier’s abilityto monitor changes in sentiment with respect to the refugee crisis.