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.

• 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