The prompt availability of information on the current state of the economy in real-time is required for prediction purposes and crucial for timely policy adjustment and economic decision-making. While important macroeconomic indicators are reported only quarterly and also published with substantial delay, other related data are available more frequently, that is monthly, weekly, daily or even more often. In this regard, the goal of nowcasting methods is to make use of such more frequently collected variables to update predictions of less often reported variables such as e.g. GDP growth. In this paper, we propose a mixed-frequency model to investigate the potential of using text data in form of newspaper articles for nowcasting German GDP growth. Newspaper text data appears to be very helpful in this regard as it directly explains economic and social progress influencing GDP growth and as it is updated frequently without any substantial delay. We compare several setups based on commonly used macro variables with and without additionally included information from text data (extracted in an unsupervised manner) as well as a setup only based on such text data. To deal with the high dimensionality of the considered data, we make use of principal component regression, penalization techniques and random forest. Comparing our results leads to the conclusion that there are certain benefits achievable when text data are included for nowcasting, but the unsupervised extraction of information from text data tends to still contain too much irrelevant noise hampering the performance of the resulting nowcasting approach.