Abstract RadioTalk is a communication platform that enabled members of the Radio Galaxy Zoo (RGZ) citizen science project to engage in discussion threads and provide further descriptions of the radio subjects they were observing in the form of tags and comments. It contains a wealth of auxiliary information which is useful for the morphology identification of complex and extended radio sources. In this paper, we present this new dataset, and for the first time in radio astronomy, we combine text and images to automatically classify radio galaxies using a multi-modal learning approach. We found incorporating text features improved classification performance which demonstrates that text annotations are rare but valuable sources of information for classifying astronomical sources, and suggests the importance of exploiting multi-modal information in future citizen science projects. We also discovered over 10000 new radio sources beyond the RGZ-DR1 catalogue in this dataset.