Twitter Geolocation and Regional Classification via Sparse Coding

Miriam Cha · Youngjune Gwon · H. T. Kung

36 citations0/0 files storedProceedings of the International AAAI Conference on Web and Social Media

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Human Mobility and Location-Based Analysis Topic Modeling Authorship Attribution and Profiling

Abstract

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.

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2012Discovering geographical topics in the twitter stream 2012Supervised Text-based Geolocation Using Language Models on an Adaptive Grid

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