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Table of Contents
Preface; Contents; 1 Having Fun?: Personalized Activity-Based Mood Prediction in Social Media; 1 Introduction; 2 Related Work; 3 Social Media Data; 3.1 Twitter Dataset; 3.2 Ground Truth; 4 Features; 5 Prediction; 5.1 Prediction Framework; 5.2 General Prediction Results; 5.3 Personalized Prediction Results; 6 Conclusion and Future Work; References; 2 Automatic Medical Image Multilingual Indexation Through a Medical Social Network; 1 Introduction; 2 Related Work; 2.1 Medical Social Networks; 2.2 Multilingual Indexation Approaches; 2.2.1 An Overview
2.2.2 Indexation Approaches via Social Networks3 Social Network Architecture Description and Implementation; 4 The Proposed Methodology; 4.1 Comments' Pre-processing; 4.2 Cleaning, Correcting, and Lemmatization; 4.2.1 Cleaning; 4.2.2 Correcting Words; 4.2.3 Lemmatization Words; 4.3 Terms' Extraction; 4.3.1 Simple Terms' Extraction; 4.3.2 Compound Terms' Extraction; 4.3.3 Concepts' Extraction; 5 Experimental Results; 5.1 Data Test and Evaluation Criteria; 5.2 Evaluation and Results of Our Approach; 6 Conclusion and Future Work; References
3 The Significant Effect of Overlapping Community Structures in Signed Social Networks1 Introduction; 1.1 Contribution of the Paper; 2 Related Work; 3 Use of Terms, Variables and Definitions; 4 Signed Disassortative Degree Mixing and Information Diffusion Approach; 4.1 Identifying Leaders; 4.2 Signed Cascading Process; 4.3 Overlapping Community-Based Ranking Algorithms; 4.3.1 Overlapping Community-Based HITS; 4.3.2 Overlapping Community-Based PageRank; 4.4 Baseline OCD Methods; 4.4.1 Signed Probabilistic Mixture Model ; 4.4.2 Multi-Objective Evolutionary Algorithm in Signed Networks
5 Sign Prediction5.1 Classifiers; 5.1.1 Logistic Regression; 5.1.2 Bagging; 5.1.3 J48; 5.1.4 Decision Table; 5.1.5 Bayesian Network and Naive Bayesian; 5.2 Sign Prediction Features; 5.2.1 Simple Degree Sign Prediction Features; 5.2.2 OC-HITS Sign Prediction; 5.2.3 OC-PageRank Sign Prediction; 6 Dataset and Metrics; 6.1 Real World Networks; 6.2 Synthetic Networks; 6.3 Evaluation Metrics; 6.3.1 Normalized Mutual Information; 6.3.2 Modularity; 6.3.3 Frustration; 7 Results; 7.1 Results of OCD; 7.1.1 Network Size n; 7.1.2 Average Node Degree k; 7.1.3 Maximum Node Degree maxk
2.2.2 Indexation Approaches via Social Networks3 Social Network Architecture Description and Implementation; 4 The Proposed Methodology; 4.1 Comments' Pre-processing; 4.2 Cleaning, Correcting, and Lemmatization; 4.2.1 Cleaning; 4.2.2 Correcting Words; 4.2.3 Lemmatization Words; 4.3 Terms' Extraction; 4.3.1 Simple Terms' Extraction; 4.3.2 Compound Terms' Extraction; 4.3.3 Concepts' Extraction; 5 Experimental Results; 5.1 Data Test and Evaluation Criteria; 5.2 Evaluation and Results of Our Approach; 6 Conclusion and Future Work; References
3 The Significant Effect of Overlapping Community Structures in Signed Social Networks1 Introduction; 1.1 Contribution of the Paper; 2 Related Work; 3 Use of Terms, Variables and Definitions; 4 Signed Disassortative Degree Mixing and Information Diffusion Approach; 4.1 Identifying Leaders; 4.2 Signed Cascading Process; 4.3 Overlapping Community-Based Ranking Algorithms; 4.3.1 Overlapping Community-Based HITS; 4.3.2 Overlapping Community-Based PageRank; 4.4 Baseline OCD Methods; 4.4.1 Signed Probabilistic Mixture Model ; 4.4.2 Multi-Objective Evolutionary Algorithm in Signed Networks
5 Sign Prediction5.1 Classifiers; 5.1.1 Logistic Regression; 5.1.2 Bagging; 5.1.3 J48; 5.1.4 Decision Table; 5.1.5 Bayesian Network and Naive Bayesian; 5.2 Sign Prediction Features; 5.2.1 Simple Degree Sign Prediction Features; 5.2.2 OC-HITS Sign Prediction; 5.2.3 OC-PageRank Sign Prediction; 6 Dataset and Metrics; 6.1 Real World Networks; 6.2 Synthetic Networks; 6.3 Evaluation Metrics; 6.3.1 Normalized Mutual Information; 6.3.2 Modularity; 6.3.3 Frustration; 7 Results; 7.1 Results of OCD; 7.1.1 Network Size n; 7.1.2 Average Node Degree k; 7.1.3 Maximum Node Degree maxk