Название: Data Analytics for Social Microblogging PlatformsАвтор: Soumi Dutta, Asit Kumar Das
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 330
Язык: английский
Формат: pdf (true), epub
Размер: 15.8 MB
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 (NLP). 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.
Twitter, Facebook, and LinkedIn are some of the most popular microblogging platforms. Millions of users send real-time messages (tweets) on various topics of interest on the Twitter microblogging site, which is one of the most popular websites on the Internet today. On a given day, popular content on Twitter (i.e., content that is discussed by a large number of people) can be used for a variety of reasons, including content suggestion and marketing and commercial campaigns. This section covers the dataset that was used in several of the book's experiments. The Twitter online social network offers an API for collecting various types of data, such as streams of tweets sent on the website and user profile information.
In the Chapter 7 we suggest a new tweet clustering technique that combines a classic clustering algorithm (K-means) with an evolutionary approach (genetic algorithms [GAs]). Despite the fact that both of these methods have been utilized for clustering in many previous works (as explained in the next section), no previous work has attempted to combine the two strategies to our knowledge.
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
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