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 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.
1. Part 1: Introduction of intelligent information filtering and organization systems for social microblogging sites - p. xxiii 2. Chapter 1: Introduction to microblogging sites - p. 3 3. Chapter 2: Literature review on data analytics for social microblogging platforms - p. 39 4. Chapter 3: Data collection using Twitter API - p. 71 5. Part 2: Microblogging dataset applications and implications - p. 91 6. Chapter 4: Attribute selection to improve spam classification - p. 95 7. Chapter 5: Ensemble summarization algorithms for microblog summarization - p. 129 8. Chapter 6: Graph-based clustering technique for microblog clustering - p. 165 9. Chapter 7: Genetic algorithm-based microblog clustering technique - p. 193 10. Part 3: Attribute selection to improve spam classification - p. 224 11. Chapter 8: Feature selection-based microblog clustering technique - p. 227 12. Chapter 9: Dimensionality reduction techniques in microblog clustering models - p. 257 13. Chapter 10: Conclusion and future directions - p. 291 14. Index - p. 297
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 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.
1. Part 1: Introduction of intelligent information filtering and organization systems for social microblogging sites - p. xxiii 2. Chapter 1: Introduction to microblogging sites - p. 3 3. Chapter 2: Literature review on data analytics for social microblogging platforms - p. 39 4. Chapter 3: Data collection using Twitter API - p. 71 5. Part 2: Microblogging dataset applications and implications - p. 91 6. Chapter 4: Attribute selection to improve spam classification - p. 95 7. Chapter 5: Ensemble summarization algorithms for microblog summarization - p. 129 8. Chapter 6: Graph-based clustering technique for microblog clustering - p. 165 9. Chapter 7: Genetic algorithm-based microblog clustering technique - p. 193 10. Part 3: Attribute selection to improve spam classification - p. 224 11. Chapter 8: Feature selection-based microblog clustering technique - p. 227 12. Chapter 9: Dimensionality reduction techniques in microblog clustering models - p. 257 13. Chapter 10: Conclusion and future directions - p. 291 14. Index - p. 297
