Twitter Geolocation and Regional Classification via Sparse Coding
Miriam Cha, Youngjune Gwon, H. T. Kung · Proceedings of the International AAAI Conference on Web and Social Media · 2021
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.