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|a UAMI
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|a Milioris, Dimitrios.
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|a Topic Detection and Classification in Social Networks :
|b the Twitter Case.
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|a Cham :
|b Springer International Publishing AG, z. Hd. Alexander Grossmann,
|c 2017.
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|a 1 online resource (113 pages)
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|a text
|b txt
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|a Preface -- Acknowledgments -- Contents -- Acronyms -- 1 Introduction -- 1.1 Dynamic Social Networks -- 1.1.1 The Twitter Social Network -- 1.2 Research and Technical Challenges -- 1.3 Problem Statement and Objectives -- 1.4 Scope and Plan of the Book -- 2 Background and Related Work -- 2.1 Introduction -- 2.2 Document-Pivot Methods -- 2.3 Feature-Pivot Methods -- 2.4 Related Work -- 2.4.1 Problem Definition -- 2.4.2 Data Preprocessing -- 2.4.3 Latent Dirichlet Allocation -- 2.4.4 Document-Pivot Topic Detection
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|a 2.4.5 Graph-Based Feature-Pivot Topic Detection2.4.6 Frequent Pattern Mining -- 2.4.7 Soft Frequent Pattern Mining -- 2.4.8 BNgram -- 2.5 Chapter Summary -- 3 Joint Sequence Complexity: Introduction and Theory -- 3.1 Introduction -- 3.2 Sequence Complexity -- 3.3 Joint Complexity -- 3.4 Contributions and Results -- 3.4.1 Models and Notations -- 3.4.2 Summary of Contributions and Results -- 3.5 Proofs of Contributions and Results -- 3.5.1 An Important Asymptotic Equivalence -- 3.5.2 Functional Equations -- 3.5.3 Double DePoissonization
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|a 3.5.4 Same Markov Sources3.5.5 Different Markov Sources -- 3.6 Expending Asymptotics and Periodic Terms -- 3.7 Numerical Experiments in Twitter -- 3.8 Suffix Trees -- 3.8.1 Examples of Suffix Trees -- 3.9 Snow Data Challenge -- 3.9.1 Topic Detection Method -- 3.9.2 Headlines -- 3.9.3 Keywords Extraction -- 3.9.4 Media URLs -- 3.9.5 Evaluation of Topic Detection -- 3.10 Tweet Classification -- 3.10.1 Tweet Augmentation -- 3.10.2 Training Phase -- 3.10.3 Run Phase -- 3.10.4 Experimental Results on Tweet Classification
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|a 3.10.4.1 Classification Performance Based on Ground Truth3.11 Chapter Summary -- 4 Text Classification via Compressive Sensing -- 4.1 Introduction -- 4.2 Compressive Sensing Theory -- 4.3 Compressive Sensing Classification -- 4.3.1 Training Phase -- 4.3.2 Run Phase -- 4.4 Tracking via Kalman Filter -- 4.5 Experimental Results -- 4.5.1 Classification Performance Based on Ground Truth -- 4.6 Chapter Summary -- 5 Extension of Joint Complexity and Compressive Sensing -- 5.1 Introduction -- 5.2 Classification Encryption via Compressed Permuted Measurement Matrices
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|a 5.2.1 Preprocessing Phase5.2.2 Run Phase -- 5.2.3 Security System Architecture -- 5.2.3.1 Privacy System -- 5.2.3.2 Key Description -- 5.2.4 Possible Attacks from Malicious Users -- 5.2.5 Experimental Results -- 5.3 Dynamic Classification Completeness -- 5.3.1 Motivation -- 5.3.2 Proposed Framework -- 5.3.3 Experimental Results -- 5.4 Stealth Encryption Based on Eulerian Circuits -- 5.4.1 Background -- 5.4.1.1 Syntax Graph -- 5.4.1.2 Eulerian Path and Circuit -- 5.4.2 Motivation and Algorithm Description -- 5.4.2.1 Motivation
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|a ""5.4.2.2 Algorithm Description""
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 630 |
0 |
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|a Twitter.
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| 630 |
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7 |
|a Twitter
|2 fast
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|a Data mining.
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|a Online social networks.
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| 650 |
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|a Exploration de données (Informatique)
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| 650 |
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|a Réseaux sociaux (Internet)
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| 650 |
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7 |
|a Data mining
|2 fast
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| 650 |
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7 |
|a Online social networks
|2 fast
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|i has work:
|a Topic Detection and Classification in Social Networks (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCXjpJpM9hDkQDwm4rJ447b
|4 https://id.oclc.org/worldcat/ontology/hasWork
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|i Print version:
|a Milioris, Dimitrios.
|t Topic Detection and Classification in Social Networks : The Twitter Case.
|d Cham : Springer International Publishing AG, z. Hd. Alexander Grossmann, ©2017
|z 9783319664132
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