Amazon cover image
Image from Amazon.com

Deep Learning for Social Media Data Analytics [electronic resource] /

Contributor(s): Material type: TextTextSeries: Studies in Big Data ; 113Publisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022Description: X, 299 p. 86 illus., 65 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031108693
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 620.00285 23
LOC classification:
  • TA345-345.5
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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. .
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

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. .

There are no comments on this title.

to post a comment.
© 2024 IIIT-Delhi, library@iiitd.ac.in