Twitter Geolocation and Regional Classification via Sparse Coding
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.
How to Cite
Cha, M., Gwon, Y., & Kung, H. (2021). Twitter Geolocation and Regional Classification via Sparse Coding. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 582-585. https://doi.org/10.1609/icwsm.v9i1.14664