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Data Analytics for Social Microblogging Platforms
- 1st Edition - November 4, 2022
- Authors: Soumi Dutta, Asit Kumar Das, Saptarshi Ghosh, Debabrata Samanta
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 7 8 5 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 7 2 3 0 - 7
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the… Read more
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Request a sales quoteData Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.
- Investigates various methodologies and algorithms for data summarization, clustering and classification
- Covers both theory and practical applications from around the world, across all related disciplines of Intelligent Information Filtering and Organization Systems
- Explores different challenges and issues related to spam filtering, attribute selection, and classification for large datasets
Researchers, professionals, and graduate students in computer science & engineering; electrical engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- About the authors
- Preface
- Acknowledgments
- About the book
- Part 1: Introduction of intelligent information filtering and organization systems for social microblogging sites
- Chapter 1: Introduction to microblogging sites
- Abstract
- 1.1. Introduction
- 1.2. Online social networking sites
- 1.3. Advantages and disadvantages of social networking
- 1.4. Microblogging sites
- 1.5. Information of social microblogging sites
- 1.6. Challenges in using microblogging sites
- 1.7. Background of the Twitter microblogging site
- 1.8. Motivation of research
- 1.9. Challenges and requirements of multi-document summarization
- 1.10. Contributions of this research
- 1.11. Conclusion
- References
- Chapter 2: Literature review on data analytics for social microblogging platforms
- Abstract
- 2.1. Introduction
- 2.2. Attribute selection and its application in spam detection
- 2.3. Summarization with various methods
- 2.4. Cluster analysis of microblogs
- 2.5. Conclusion
- References
- Chapter 3: Data collection using Twitter API
- Abstract
- 3.1. Introduction
- 3.2. Experimental dataset description
- 3.3. Data preprocessing
- 3.4. Removal of user names and URLs
- 3.5. Converting emojis and emoticons to words
- 3.6. Conclusion
- References
- Part 2: Microblogging dataset applications and implications
- Chapter 4: Attribute selection to improve spam classification
- Abstract
- 4.1. Introduction
- 4.2. Literature survey
- 4.3. Methodology for classification
- 4.4. Experimental dataset
- 4.5. Evaluating performance
- 4.6. Conclusion
- References
- Chapter 5: Ensemble summarization algorithms for microblog summarization
- Abstract
- 5.1. Introduction
- 5.2. Base summarization algorithms
- 5.3. Unsupervised ensemble summarization
- 5.4. Supervised ensemble summarization
- 5.5. Experiments and results
- 5.6. Demonstrating the input and output of summarization algorithms through an example
- 5.7. Conclusion
- References
- Chapter 6: Graph-based clustering technique for microblog clustering
- Abstract
- 6.1. Introduction
- 6.2. Related work
- 6.3. Background studies
- 6.4. Proposed methodology
- 6.5. Results and discussion
- 6.6. Conclusion
- References
- Chapter 7: Genetic algorithm-based microblog clustering technique
- Abstract
- 7.1. Introduction
- 7.2. Related work
- 7.3. Clustering using genetic algorithms and K-means
- 7.4. Evaluating performance
- 7.5. Experimental dataset
- 7.6. Conclusion
- References
- Part 3: Attribute selection to improve spam classification
- Chapter 8: Feature selection-based microblog clustering technique
- Abstract
- 8.1. Introduction
- 8.2. Related work
- 8.3. Microblog clustering algorithms
- 8.4. Dataset for clustering algorithms
- 8.5. Experimental results
- 8.6. Conclusion
- References
- Chapter 9: Dimensionality reduction techniques in microblog clustering models
- Abstract
- 9.1. Introduction
- 9.2. Literature survey
- 9.3. Proposed methodology
- 9.4. Dataset
- 9.5. Results and discussion
- 9.6. Conclusion
- References
- Chapter 10: Conclusion and future directions
- Abstract
- 10.1. Introduction
- 10.2. Summary of contributions
- 10.3. Future research directions
- References
- Index
- No. of pages: 328
- Language: English
- Edition: 1
- Published: November 4, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323917858
- eBook ISBN: 9780323972307
SD
Soumi Dutta
Soumi Dutta works in the Institute of Engineering and Management, Kolkata, West Bengal, India.
Affiliations and expertise
Institute of Engineering and Management, Kolkata, West Bengal, IndiaAD
Asit Kumar Das
Asit Kumar Das is Professor of Computer Science and Technology, at the Indian Institute of Engineering Science and Technology Shibpur, Howrah. He is also the Head of the Center of Healthcare Science and Technology of the Institute. His area of research interest includes data mining and pattern recognition, social networks, bioinformatics, machine learning and soft computing, text, audio and video data analysis.
Affiliations and expertise
Indian Institute of Engineering Science and Technology, Shibpur, IndiaSG
Saptarshi Ghosh
Dr. Ghosh is an Assistant Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India. His primary research interests are in social network analysis, legal data analytics, and algorithmic bias and fairness. His research uses techniques from machine learning, natural language processing, information retrieval, and complex network theory. He received his PhD in Computer Science from IIT Kharagpur in 2013. He is a Humboldt Post-doctoral research fellow at the Max Planck Institute for Software Systems (MPI-SWS), Germany. He has also been an Assistant Professor at the Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, India.
Affiliations and expertise
Indian Institute of Technology Kharagpur, IndiaDS
Debabrata Samanta
Debabrata Samanta is an Assistant Professor & Program Head, at the Department of Computing and Information Technologies, Rochester Institute of Technology, Kosovo, Europe. He obtained his Ph.D. in Computer Science and Engg. in the area of SAR Image Processing. He is keenly interested in Interdisciplinary Research and development and has experience spanning fields of SAR Image Analysis, Video surveillance, a Heuristic algorithm for Image Classification, Deep Learning Framework for Detection and Classification, Blockchain, Statistical Modelling, Wireless Adhoc Networks, Natural Language Processing. He has successfully completed six Consultancy Projects. He owns 22 Patents (4 Design Indian Patents and 2 Australian patents Granted, 16 Indian Patents published) and 2 copyrights. He has authored or co-authored over 224 research papers; he has co-authored 13 books and co-edited 13 books. He has presented various papers at international conferences and received Best Paper awards. He is an IEEE Senior Member, an Associate Life Member of the Computer Society of India (CSI), and a Life Member of the Indian Society for Technical Education (ISTE).
Affiliations and expertise
Department of Computing and Information Technologies, Rochester Institute of Technology, Republic of KosovoRead Data Analytics for Social Microblogging Platforms on ScienceDirect