twitter geolocation and regional classification
Twitter Geolocation and Regional Classification via Sparse Coding
Cha, Miriam (Harvard University) | Gwon, Youngjune (Harvard University) | Kung, H. T. (Harvard University)
We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-based grid selection, we lower geolocation errors and improve classification accuracy from previously known results on the GEOTEXT dataset.