Advanced Data Mining Tools and Methods for Social Computing

Advanced Data Mining Tools and Methods for Social Computing

1st Edition - January 14, 2022

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  • Editors: Sourav De, Sandip Dey, Siddhartha Bhattacharyya, Surbhi Bhatia
  • eBook ISBN: 9780323857093
  • Paperback ISBN: 9780323857086

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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 analysis
  • Covers a broad range of data mining tools and methods for social computing and analysis
  • Includes practical examples and case studies across a range of tools and methods
  • Features coding examples and supplementary data sets in every chapter


Researchers, professionals, and graduate students in computer science & engineering, bioinformatics, and electrical engineering (primary)

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Dedication
  • List of contributors
  • Preface
  • Chapter 1: An introduction to data mining in social networks
  • Abstract
  • 1.1. Introduction
  • 1.2. Data mining concepts
  • 1.3. Social computing
  • 1.4. Clustering and classification
  • References
  • Chapter 2: Performance tuning of Android applications using clustering and optimization heuristics
  • Abstract
  • 2.1. Introduction
  • 2.2. Related work
  • 2.3. Research methodology
  • 2.4. Subject applications
  • 2.5. Implementation phase 1 – clustering and knapsack solvers
  • 2.6. Implementation phase 2 – Ant colony optimization
  • 2.7. Results and findings
  • 2.8. Threats to validity
  • 2.9. Conclusion
  • References
  • Chapter 3: Sentiment analysis of social media data evolved from COVID-19 cases – Maharashtra
  • Abstract
  • 3.1. Introduction
  • 3.2. Literature review
  • 3.3. Proposed design
  • 3.4. Analysis and predictions
  • 3.5. Conclusion
  • 3.6. Acknowledgment
  • References
  • Chapter 4: COVID-19 outbreak analysis and prediction using statistical learning
  • Abstract
  • 4.1. Introduction
  • 4.2. Related literature
  • 4.3. Proposed model
  • 4.4. Prophet
  • 4.5. Results and discussion
  • 4.6. Conclusion
  • References
  • Chapter 5: Verbal sentiment analysis and detection using recurrent neural network
  • Abstract
  • 5.1. Introduction
  • 5.2. Sources for sentiment detection
  • 5.3. Literature survey
  • 5.4. Machine learning techniques for sentiment analysis
  • 5.5. Proposed method
  • 5.6. Results and discussion
  • 5.7. Conclusions
  • References
  • Chapter 6: A machine learning approach to aid paralysis patients using EMG signals
  • Abstract
  • 6.1. Introduction
  • 6.2. Associated works
  • 6.3. System model
  • 6.4. Simulation and results
  • 6.5. Conclusion
  • References
  • Chapter 7: Influence of traveling on social behavior
  • Abstract
  • 7.1. Introduction
  • 7.2. Related work
  • 7.3. Importance of social networking in real life
  • 7.4. Dynamics of traveling
  • 7.5. Dynamics-based social behavior analysis
  • 7.6. Recognition of human social behavior using machine learning techniques
  • 7.7. Conclusion
  • References
  • Chapter 8: A study on behavior analysis in social network
  • Abstract
  • 8.1. Introduction
  • 8.2. Basic concepts of behavior analysis in social networks
  • 8.3. Uses of behavior analysis in social networks
  • 8.4. Future direction
  • 8.5. Conclusion
  • References
  • Chapter 9: Recent trends in recommendation systems and sentiment analysis
  • Abstract
  • 9.1. Introduction
  • 9.2. Basic terms and concepts of sentiment analysis and recommendation systems
  • 9.3. Overview of sentiment analysis approaches in recommendation systems
  • 9.4. Recent developments (related work)
  • 9.5. Challenges
  • 9.6. Future direction
  • 9.7. Conclusion
  • References
  • Chapter 10: Data visualization: existing tools and techniques
  • Abstract
  • 10.1. Introduction
  • 10.2. Prior research works on data visualization issues
  • 10.3. Challenges during visualization of innumerable data
  • 10.4. Existing data visualization tools and techniques with key characteristics
  • 10.5. Conclusion
  • References
  • Chapter 11: An intelligent agent to mine for frequent patterns in uncertain graphs
  • Abstract
  • 11.1. Introduction
  • 11.2. Related work
  • 11.3. Mining graphs and uncertainty
  • 11.4. Methodology
  • 11.5. Implementation
  • 11.6. Conclusion
  • 11.7. Future directions
  • References
  • Chapter 12: Mining challenges in large-scale IoT data framework – a machine learning perspective
  • Abstract
  • 12.1. Introduction
  • 12.2. Review of literature
  • 12.3. Proposed work
  • 12.4. Application framework
  • 12.5. H2O work flow environment
  • 12.6. Experimental results
  • 12.7. Discussion and conclusion
  • References
  • Chapter 13: Conclusion
  • Abstract
  • References
  • Index

Product details

  • No. of pages: 292
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: January 14, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780323857093
  • Paperback ISBN: 9780323857086

About the Editors

Sourav De

Dr. Sourav De completed his PhD in Computer Science and Technology at the Indian Institute of Engineering & Technology, Shibpur, Howrah, India in 2015. He is currently an Associate Professor of Computer Science & Engineering at Cooch Behar Government Engineering College, West Bengal. He is a co-author of one book, the co-editor of twelve books, and has more than 54 research publications in internationally reputed journals, international edited books, international IEEE conference proceedings, and one patent to his credit. His research interests include soft computing, pattern recognition, image processing, and data mining. Dr. De is a senior member of IEEE and a member of ACM, Institute of Engineers (IEI), Computer Science Teachers Association (CSTA), Institute of Engineers and IAENG, Hong Kong. He is a life member of ISTE, India.

Affiliations and Expertise

Associate Professor of Computer Science and Engineering, Cooch Behar Government Engineering College, Cooch Behar, West Bengal, India

Sandip Dey

Dr. Sandip Dey completed his PhD in Computer Science and Engineering at Jadavpur University, India in 2016. He is currently an Assistant Professor in the Department of Computer Science at Sukanta Mahavidyalaya, Jalpaiguri. He has more than 40 research publications in international journals, book chapters and conference proceedings to his credit. He has authored or edited four books, published by John Wiley & Sons and Elsevier. His research interests include soft computing, quantum computing and image analysis.

Affiliations and Expertise

Associate Professor, Department of Computer Science, Sukanta Mahavidyalaya, Jalpaiguri, Dhupguri, West Bengal, India

Siddhartha Bhattacharyya

Siddhartha Bhattacharyya [FRSA, FIET (UK), FIEI, FIETE, LFOSI, SMIEEE, SMACM, SMIETI, LMCSI, LMISTE] is currently the Principal of Rajnagar Mahavidyalaya, Birbhum, India. Prior to this, he was a Professor at CHRIST (Deemed to be University), Bangalore, India. He also served as the Principal of RCC Institute of Information Technology, Kolkata, India. He has served VSB Technical University of Ostrava, Czech Republic as a Senior Research Scientist. He is the recipient of several coveted national and international awards. He received the Honorary Doctorate Award (D. Litt.) from the University of South America and the SEARCC International Digital Award ICT Educator of the Year in 2017. He was appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He has been appointed as the IEEE Computer Society Distinguished Visitor for the tenure 2021-2023. He is a co-author of 6 books and the co-editor of 75 books and has more than 300 research publications in international journals and conference proceedings to his credit.

Affiliations and Expertise

Principal, Rajnagar Mahavidyalaya, Birbhum, India

Surbhi Bhatia

Dr. Surbhi Bhatia is doctorate in Computer Science and Engineering from Banasthali Vidypaith, India. She earned Project management Professional Certification from reputed Project Management Institute, USA. She is currently an Assistant Professor in the College of Computer Sciences and Information Technology, King Faisal University, Saudi Arabia. She has more than 8 years of teaching and academic experience. She has published more than 40 papers in reputed journals and conferences in high indexing databases and has and has patents from US, Australia and India.  She has delivered talks as keynote speaker in IEEE conferences and participated in AICTE sponsored faculty development programs. She has successfully authored 2 books and edited 7 books from Wiley, CRC Press, Elsevier and Springer.  She has successfully been awarded 2 funded research project grants from Deanship of Scientific Research at King Faisal University and also from Ministry of Education, Saudi Arabia. Her research interests include Machine Learning, Sentiment analysis and Information Retrieval.

Affiliations and Expertise

Assistant Professor, College of Computer Science and Information Technology, King Faisal University, Riyadh, Saudi Arabia

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