In this paper we propose a text-based geolocation method based on neural networks. Our contributions are as follows: (1) we achieve state-of-the-art results on benchmark Twitter geolocation datasets; (2) we show that the model is less sensitive to the specific location discretisation method; (3) we release the first broad-coverage dataset for evaluation of lexical dialectology models; (4) we incorporate our text-based model into a network-based model<cite> (Rahimi et al., 2015a)</cite> and improve the performance utilising both network and text; and (5) we use the model's embeddings for extraction of local terms and show that it outperforms two baselines.