001449903 000__ 03535cam\a2200541\i\4500
001449903 001__ 1449903
001449903 003__ OCoLC
001449903 005__ 20230310004424.0
001449903 006__ m\\\\\o\\d\\\\\\\\
001449903 007__ cr\cn\nnnunnun
001449903 008__ 220929s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001449903 019__ $$a1345580304
001449903 020__ $$a9783031108693$$q(electronic bk.)
001449903 020__ $$a3031108698$$q(electronic bk.)
001449903 020__ $$z303110868X
001449903 020__ $$z9783031108686
001449903 0247_ $$a10.1007/978-3-031-10869-3$$2doi
001449903 035__ $$aSP(OCoLC)1346251157
001449903 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ
001449903 049__ $$aISEA
001449903 050_4 $$aQ325.73
001449903 08204 $$a006.3/1$$223/eng/20220929
001449903 24500 $$aDeep learning for social media data analytics /$$cTzung-Pei Hong, Leticia Serrano-Estrada, Akrati Saxena, Anupam Biswas, editors.
001449903 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001449903 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and color).
001449903 336__ $$atext$$btxt$$2rdacontent
001449903 337__ $$acomputer$$bc$$2rdamedia
001449903 338__ $$aonline resource$$bcr$$2rdacarrier
001449903 4901_ $$aStudies in big data ;$$vvolume 113
001449903 5050_ $$aNode Classification using Deep Learning in Social Networks -- NN-LP-CF: Neural Network based Link Prediction on Social Networks using Centrality-based Features -- Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review -- Convolutional and Recurrent Neural Networks for Opinion Mining on Drug Reviews -- Text-based Sentiment Analysis using Deep Learning Techniques -- Social Sentiment Analysis Using Features based Intelligent Learning Techniques.
001449903 506__ $$aAccess limited to authorized users.
001449903 520__ $$aThis edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics. .
001449903 588__ $$aDescription based on print version record.
001449903 650_0 $$aDeep learning (Machine learning)
001449903 650_0 $$aSocial media$$xData processing.
001449903 655_0 $$aElectronic books.
001449903 7001_ $$aHong, Tzung-Pei,$$d1963-$$eeditor.
001449903 7001_ $$aSerrano-Estrada, Leticia,$$eeditor.
001449903 7001_ $$aSaxena, Akrati,$$eeditor.
001449903 7001_ $$aBiswas, Anupam,$$eeditor.
001449903 77608 $$iPrint version:$$tDEEP LEARNING FOR SOCIAL MEDIA DATA ANALYTICS.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2022$$z303110868X$$w(OCoLC)1330407356
001449903 830_0 $$aStudies in big data ;$$vv. 113.
001449903 852__ $$bebk
001449903 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-10869-3$$zOnline Access$$91397441.1
001449903 909CO $$ooai:library.usi.edu:1449903$$pGLOBAL_SET
001449903 980__ $$aBIB
001449903 980__ $$aEBOOK
001449903 982__ $$aEbook
001449903 983__ $$aOnline
001449903 994__ $$a92$$bISE