Abstract
Despite the research boom on words embeddings and their text mining applications from the last years, the vast majority of publications focus only on the English language. Furthermore, hyperparameter tuning is a rarely well documented process (specially for non English text) that is necessary to obtain high quality word representations. In this work, we present how different hyperparameter combinations impact the resulting German word vectors and how these word representations can be part of more complex models. In particular, we perform first an intrinsic evaluation of our German word embeddings, which are later used within a predictive sentiment analysis model. The latter does not only serve as an extrinsic evaluation of the German word embeddings but also shows the feasibility of predic ting preferences only from document embeddings.