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Deep learning for social media data ...

Hong, Tzung-Pei.

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Deep learning for social media data analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for social media data analytics/ edited by Tzung-Pei Hong ... [et al.].
other author:	Hong, Tzung-Pei.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	x, 299 p. :ill., digital ;24 cm.
[NT 15003449]:	Node 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.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-031-10869-3
ISBN:	9783031108693

Deep learning for social media data analytics
Deep learning for social media data analytics[electronic resource] /edited by Tzung-Pei Hong ... [et al.]. - Cham :Springer International Publishing :2022. - x, 299 p. :ill., digital ;24 cm. - Studies in big data,v. 1132197-6511 ;. - Studies in big data ;v. 113..

Node 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.

This 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.

ISBN: 9783031108693

Standard No.: 10.1007/978-3-031-10869-3doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .D44 2022

Dewey Class. No.: 006.31

Deep learning for social media data analytics
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490 1 $a Studies in big data, $x 2197-6511 ; $v v. 113
505 0 $a Node 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 $a This 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 $a Deep learning (Machine learning) $3 3538509
650 0 $a Social media $x Data processing. $3 3512745
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Cyber-Physical Systems. $3 3591993
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Social Media. $3 2186794
700 1 $a Hong, Tzung-Pei. $3 1006343
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