Media bias may often affect individuals’ opinions on reported topics.Many existing methods that aim to identify such bias forms em-ploy individual, specialized techniques and focus only on Englishtexts. We propose to combine the state-of-the-art in order to furtherimprove the performance in bias identification. Our prototype con-sists of three analysis components to identify media bias words inGerman news articles. We use an IDF-based component, a compo-nent utilizing a topic-dependent bias dictionary created using wordembeddings, and an extensive dictionary of German emotionalterms compiled from multiple sources. Finally, we discuss two notyet implemented analysis components that use machine learningand network analysis to identify media bias. All dictionary-basedanalysis components are experimentally extended with the use ofgeneral word embeddings. We also show the results of a user study