Discovering context: classifying tweets through a semantic transform based on wikipedia

Authors:
Yegin Genc;Yasuaki Sakamoto;Jeffrey V. Nickerson
Affiliations:
Center for Decision Technologies, Stevens Institute of Technology, Hoboken, NJ;Center for Decision Technologies, Stevens Institute of Technology, Hoboken, NJ;Center for Decision Technologies, Stevens Institute of Technology, Hoboken, NJ
Venue:
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
Year:
2011

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Abstract

By mapping messages into a large context, we can compute the distances between them, and then classify them. We test this conjecture on Twitter messages: Messages are mapped onto their most similar Wikipedia pages, and the distances between pages are used as a proxy for the distances between messages. This technique yields more accurate classification of a set of Twitter messages than alternative techniques using string edit distance and latent semantic analysis.