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Social Media and Machine Learning / Alberto Cano (2020)  

Public Titre : Social Media and Machine Learning Type de document : Monographie Auteurs : Alberto Cano, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 96 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-83880-616-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] données massives [Termes IGN] exploration de texte [Termes IGN] langage naturel (informatique) [Termes IGN] réseau social [Termes IGN] sentiment Résumé : (éditeur) Social media has transformed society and the way people interact with each other. The volume and speed in which new content is being generated surpasses the processing capacity of machine learning systems. Analyzing such data demands new approaches coming from natural language processing, text mining, sentiment analysis, etc to understand and resolve the arising challenges. There is a need to develop robust and adaptable systems to tackle these open issues in real time, as well as to provide a meaningful summarization and visualization to the end users. This book provides the reader with a comprehensive overview of the latest developments in social media and machine learning, addressing research innovations, applications, trends, and open challenges in this crucial area. Note de contenu : 1- Introductory chapter: Data streams and online learning in social media 2- Automatic speech emotion recognition using machine learning 3- A case study of using big data processing in education: Method of matching members by optimizing collaborative learning environment 4- Literature review on big data analytics methods 5- Information and communication based collaborative learning and behavior modeling using machine learning algorithm Numéro de notice : 28481 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78089 En ligne : https://doi.org/10.5772/intechopen.78089 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99165