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020 _a9783031108693
_9978-3-031-10869-3
024 7 _a10.1007/978-3-031-10869-3
_2doi
050 4 _aTA345-345.5
072 7 _aUN
_2bicssc
072 7 _aCOM018000
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072 7 _aUN
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082 0 4 _a620.00285
_223
245 1 0 _aDeep Learning for Social Media Data Analytics
_h[electronic resource] /
_cedited by Tzung-Pei Hong, Leticia Serrano-Estrada, Akrati Saxena, Anupam Biswas.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aX, 299 p. 86 illus., 65 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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. .
650 0 _aEngineering
_xData processing.
650 0 _aCooperating objects (Computer systems).
650 0 _aComputational intelligence.
650 0 _aBig data.
650 0 _aSocial media.
650 1 4 _aData Engineering.
650 2 4 _aCyber-Physical Systems.
650 2 4 _aComputational Intelligence.
650 2 4 _aBig Data.
650 2 4 _aSocial Media.
700 1 _aHong, Tzung-Pei.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSerrano-Estrada, Leticia.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSaxena, Akrati.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBiswas, Anupam.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031108686
776 0 8 _iPrinted edition:
_z9783031108709
776 0 8 _iPrinted edition:
_z9783031108716
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v113
856 4 0 _uhttps://doi.org/10.1007/978-3-031-10869-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173525
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