Transparency, participation, and collaboration are the core pillars of open government. For the systematic integration of citizens and other stakeholders into the policy and public value creation process, their opinions, wishes, and complaints first need to be received. In the future, including user-generated content from social media will become a main channel for the enrichment of this information base for public administrative bodies and commercial firms. However, the sheer speed of growth of this constantly updated data pool makes manual work infeasible. The automated gathering, combination, analysis, and visualization of user-generated content from various sources and multiple languages is therefore imperative. In this study, we present a design science research approach to develop a general framework ('MarketMiner') to handle large amounts of foreign-language user-generated content. As a first empirical application, we implement the framework in the automotive industry by analyzing Chinese automotive forums for the benefit of English-speaking users. At the same time, the ideas, methods, and insights are transferred to the public sector context, especially in light of the current challenges of a high number of political refugees from Arabic countries entering into the European Union. The results are promising in that MarketMiner can dramatically improve the utilization of multi-language, multi-source social media content. The modular set-up of the artifact allows an easy transfer to additional areas of application.