Compressed Sensing Classification in Online Social Networks

Abstract : In this technical report we introduce a novel low dimensional method to perform topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection. Then, based on the nature of the data, we apply the theory of Compressive Sensing to perform topic classification by recovering an indicator vector, while reducing significantly the amount of information from tweets. We exploit datasets in various languages collected by using the Twitter streaming API, and achieve increased classification accuracy when comparing to state-of-the-art methods based on bag-of-words, along with several reconstruction techniques.
Type de document :
Pré-publication, Document de travail
Columbia University, New York. 2014
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Contributeur : Dimitrios Milioris <>
Soumis le : vendredi 8 avril 2016 - 04:04:09
Dernière modification le : jeudi 9 février 2017 - 15:17:16
Document(s) archivé(s) le : lundi 14 novembre 2016 - 22:14:06


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  • HAL Id : hal-01299628, version 1



Dimitrios Milioris. Compressed Sensing Classification in Online Social Networks. Columbia University, New York. 2014. 〈hal-01299628〉



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