13 9 Sentiment Analysis-Based Extraction of Real-Time Social Media Information From Twitter Using Natural Language Processing 9.1 Introduction 9.2 Literature Survey 9.3 Implementation and Results 9.4 Conclusion 9.5 Future Scope References
14 10 Cascading Behavior: Concept and Models 10.1 Introduction 10.2 Cascade Networks 10.3 Importance of Cascades 10.4 Purposes for Studying Cascades 10.5 Collective Action 10.6 Cascade Capacity 10.7 Models of Network Cascades 10.8 Centrality 10.9 Cascading Failures 10.10 Cascading Behavior Example Using Python 10.11 Conclusion References
15 11 Exploring Social Networking Data Sets 11.1 Introduction 11.2 Establishing a Social Network 11.3 Connectivity of Users in Social Networks 11.4 Centrality Measures in Social Networks 11.5 Case Study of Facebook 11.6 Conclusion References
16 Index
17 Wiley End User License Agreement
List of Tables
1 Chapter 6Table 6.1 Questionnaires and responses of participants.
2 Chapter 7Table 7.1 Performance evaluation for the KDD99 data set without using the Featur...
3 Chapter 8Table 8.1 Twitter data set.Table 8.2 User data set is inactive.Table 8.3 Forecasting the word “happy” in inactive users.Table 8.4 Term data set active social media users.Table 8.5 Data set of active user.Table 8.6 Error and MAPE.
4 Chapter 9Table 9.1 The characteristics of social media platforms.Table 9.2 Literature survey summary.
List of Illustrations
1 Chapter 1Figure 1.1 Social network analysis.Figure 1.2 Social network analysis using Python.Figure 1.3 Flowchart of social network.
2 Chapter 2Figure 2.1 Comparison directed and undirected graph.Figure 2.2 Simple graph.Figure 2.3 Multigraph.Figure 2.4 Weighted graph.Figure 2.5 Unweighted graph.Figure 2.6 NodeXL network overview discovery and exploration in excel [5].Figure 2.7 Python official documentation.Figure 2.8 Anaconda navigator.Figure 2.9 Conda environment installation.Figure 2.10 QR code for workbooks and source codes.Figure 2.11 Code blocks for importing libraries.Figure 2.12 Code block for reading data.Figure 2.13 Code block for reading edge list.Figure 2.14 Visualization of Facebook users.Figure 2.15 Code block for centrality measures.Figure 2.16 Visualization of centrality measures on Facebook users.Figure 2.17 Visualization of graph database used in business.
3 Chapter 3Figure 3.1 The presentation of a small-scale network based on the interpersonal ...Figure 3.2 E-mail exchanges between company employees [2].Figure 3.3 Links between web blogs as a form of a large network [2].Figure 3.4 (a) Directed graph with 4 nodes [4]. (b) A graph with 4 nodes [4].Figure 3.5 Social networks quickly expand to reach many people [6].Figure 3.6 A small network that has one highly connected node, or hub [7].Figure 3.7 Scale-free network in vast size where the hub nodes could be distingu...
4 Chapter 4Figure 4.1 Aspects of cascading behavioral pattern.Figure 4.2 Cascading behavior in OSN.
5 Chapter 5Figure 5.1 Social network showing links and nodes in healthcare.Figure 5.2 Diagrammatic representation of existing methods.Figure 5.3 Diagrammatic representation of graph theory.
6 Chapter 6Figure 6.1 Building social semantic ontology for specific domain.Figure 6.2 Code for building agriculture ontology.Figure 6.3 Code for building object property.Figure 6.4 Usage of class crop in agriculture ontology.Figure 6.5 Code for defining restrictions in object property.Figure 6.6 Ontology graph for agriculture ontology.Figure 6.7 Code for social ontology.Figure 6.8
