Twitter Geolocation and Regional Classification via Sparse Coding

Proceedings of the International AAAI Conference on Web and Social Media, 9

Volume

Vol. 9 No. 1 (2015): Ninth International AAAI Conference on Web and Social Media

Poster Papers

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.

10.1609/icwsm.v9i1.14664