Topic Detection Completeness in Twitter: Is it Possible?

Abstract : In this paper we study the application of Matrix Completion in topic detection in Twitter. The proposed method first employs Joint Complexity, which is defined as the cardinality of a set of all distinct factors of a given string represented by suffix trees, to perform topic detection. We exploit correlations in the data, and apply the theory of Matrix Completion to build complete matrices from a small number of random sample Joint Complexity scores. The experimental evaluation with real data from Twitter presents the topic detection accuracy based on complete reconstructed matrices, without making an exhaustive computation of Joint Complexity scores, while maintaining a low computational complexity.
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Communication dans un congrès
International Conference on Computational Social Science (ICCSS), Jun 2016, Chicago, United States
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https://hal-polytechnique.archives-ouvertes.fr/hal-01299630
Contributeur : Dimitrios Milioris <>
Soumis le : vendredi 8 avril 2016 - 04:30:33
Dernière modification le : jeudi 9 février 2017 - 15:17:16

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  • HAL Id : hal-01299630, version 1

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Dimitrios Milioris, Dániel Kondor. Topic Detection Completeness in Twitter: Is it Possible?. International Conference on Computational Social Science (ICCSS), Jun 2016, Chicago, United States. 〈hal-01299630〉

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