Social media posts created by customers capture a lot of business relevant information for decision-makers, e.g., current consumer expectations on products and services. For that purpose, the social media posts need to be analyzed thoroughly. In this respect, a topic-related classification facilitates managerial decision-making because business-relevant topics, social media users discuss about, immediately become obvious and the need for action can be derived. For instance, it may get obvious that the majority of a company’s negative customer posts refers to a particular product or a specific campaign. However, such a classification of social media posts is particularly challenging for small and medium-sized enterprises (SMEs). This is because human resources for a manual examination of posts are missing and an automatic analysis is error-prone due to particularities of customer posts such as the occurrence of regional dialect or branch-specific expressions. We thus develop a tool, which enables the automatized topic-related classification of social media posts and matches the particular requirements of SMEs in southern Germany. Our solution is evaluated by using a data set stemming from three collaborating companies.