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Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets and 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 will focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data to address the most innovative developments, as well as research issues and solutions in massive data computations and their applications. 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 data sets
