Microblog geolocation estimation with recurrent neural networks

Abstract

People collect and use information about real world from internet to help their daily activities. In particular, the number of users in microblog such as Twitter is so large that users can get a diversity of information. They can elicit not only the information which they need from microblog posts but also the location which is indicated by the contents posted in microblog. While previous approaches apply corpus-based or machine learning that require various prior knowledge such as natural language processing and feature engineering, our approach is able to estimate the location without those requirements with extension of long-short term memory (LSTM). In our experiment, we apply our approach to geo-tagged tweets posted in Twitter and show that this approach is effective in outperforming corpus-based and previous works that use support vector machine (SVM) with bag-of-words (BoW).