Twitter Geolocation and Regional Classification via Sparse Coding
Miriam Cha, Youngjune Gwon, H. T. Kung
2021Proceedings of the International AAAI Conference on Web and Social Media36 citationsDOIOpen Access PDF
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.
Topics & Concepts
GeolocationComputer scienceCoding (social sciences)Feature selectionNeural codingArtificial intelligencePattern recognition (psychology)GridRaw dataFeature (linguistics)VotingData miningGeographyWorld Wide WebMathematicsGeodesyPoliticsLinguisticsLawStatisticsProgramming languagePhilosophyPolitical scienceAdvanced Image and Video Retrieval TechniquesIndoor and Outdoor Localization TechnologiesSparse and Compressive Sensing Techniques