About the Book:
Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis.
Key Features
Provides insights into the latest research trends in social network analysisCovers a broad range of data mining tools and methods for social computing and analysisIncludes practical examples and case studies across a range of tools and methodsFeatures coding examples and supplementary data sets in every chapter Table of ContentsChapter 1: An introduction to data mining in social networks 1.1. Data mining concepts
1.2. Social computing
1.3. Clustering and classification
Chapter 2: Performance tuning of Android applications using clustering and optimization heuristics 2.1. Introduction
2.2. Research methodology
2.3. Subject applications
2.4. Implementation phase 1 – clustering and knapsack solvers
2.5. Implementation phase 2 – Ant colony optimization
Chapter 3: Sentiment analysis of social media data evolved from COVID-19 cases – Maharashtra 3.1. Introduction
3.2. Literature review
3.3. Proposed design
3.4. Analysis and predictions
Chapter 4: COVID-19 outbreak analysis and prediction using statistical learning 4.1. Introduction
4.2. Related literature
4.3. Proposed model
Chapter 5: Verbal sentiment analysis and detection using recurrent neural network
5.1. Introduction
5.2. Sources for sentiment detection
5.3. Literature survey
5.4. Machine learning techniques for sentiment analysis
Chapter 6: A machine learning approach to aid paralysis patients using EMG signals
6.1. Introduction
6.2. Associated works
6.3. System model
Chapter 7: Influence of traveling on social behavior
7.1. Introduction
7.2. Importance of social networking in real life
7.3. Dynamics of traveling
7.4. Dynamics-based social behavior analysis
Chapter 8: A study on behavior analysis in social network 8.1. Basic concepts of behavior analysis in social networks
8.2. Uses of behavior analysis in social networks
Chapter 9: Recent trends in recommendation systems and sentiment analysis 9.1. Basic terms and concepts of sentiment analysis and recommendation systems
9.2. Overview of sentiment analysis approaches in recommendation systems
Chapter 10: Data visualization: existing tools and techniques
10.1. Prior research works on data visualization issues
10.2. Challenges during visualization of innumerable data
10.3. Existing data visualization tools and techniques with key characteristics
Chapter 11: An intelligent agent to mine for frequent patterns in uncertain graphs 11.1. Introduction
11.2. Mining graphs and uncertainty
11.3. Methodology
11.4. Implementation
Chapter 12: Mining challenges in large-scale IoT data framework – a machine learning perspective 12.1. Proposed work
12.2. Application framework
12.3. H2O work flow environment
Chapter 13: Conclusion
