000			03332nam a22004095i 4500
001			393988
003			ES-MaUEC
005			20230102123045.0
007			cr nn 008mamaa
008			220918s2022 sz | s |||| 0|eng d
020			_a9783031108693
024	7		_a10.1007/978-3-031-10869-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aDeep Learning for Social Media Data Analytics _cedited by Tzung-Pei Hong, Leticia Serrano-Estrada, Akrati Saxena, Anupam Biswas
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 299 páginas) _b86 ilustraciones, 65 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aStudies in Big Data _x2197-6511 _v113
505	0		_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.
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. .
700	1		_aHong, Tzung-Pei _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSerrano-Estrada, Leticia _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSaxena, Akrati _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBiswas, Anupam _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031108686
776	0	8	_iPrinted edition: _z9783031108709
776	0	8	_iPrinted edition: _z9783031108716
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-10869-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c393988 _d393988