Skip to main content
SpringerLink
Log in

Deep Learning for Social Media Data Analytics

  • Book
  • © 2022

Overview

Editors:
  1. Tzung-Pei Hong

    1. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Leticia Serrano-Estrada

    1. Urban Design and Regional Planning Unit, University of Alicante, Alicante, Spain

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Akrati Saxena

    1. Dept of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Anupam Biswas

    1. Department of Computer Science and Engineering, National Institute Of Technology Silchar, Cachar, India

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Covers ongoing research in both theory and practical applications
  • Presents recent research on deep learning for social media data analytics
  • Shows challenges emerged from the volume of social media data

Part of the book series: Studies in Big Data (SBD, volume 113)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (15 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Network Structure Analysis

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Node Classification Using Deep Learning in Social Networks

      • Aikta Arya, Pradumn Kumar Pandey, Akrati Saxena
      Pages 3-26

    3. NN-LP-CF: Neural Network Based Link Prediction on Social Networks Using Centrality-Based Features

      • Shashank Sheshar Singh, Divya Srivastva, Ajay Kumar, Vishal Srivastava
      Pages 27-42

  3. Social Media Text Analysis

    1. Front Matter

      Pages 43-43
      Download chapter PDF

    2. Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review

      • Rrubaa Panchendrarajan, Akrati Saxena
      Pages 45-63

    3. Convolutional and Recurrent Neural Networks for Opinion Mining on Drug Reviews

      • Nesma Settouti, Fatiha Youbi
      Pages 65-79

    4. Text-Based Sentiment Analysis Using Deep Learning Techniques

      • Siddhi Kadu, Bharti Joshi
      Pages 81-100

    5. Social Sentiment Analysis Using Features Based Intelligent Learning Techniques

      • Prasannavenkatesan Theerthagiri
      Pages 101-117

  4. User Behaviour Analysis

    1. Front Matter

      Pages 119-119
      Download chapter PDF

    2. Modified-PIP with Deep Neural Network (DNN) Architecture: A Coherent Recommendation Framework for Capturing User Behaviour

      • Bam Bahadur Sinha, Gurvinder Singh Yadav, Sagar Badrish Kudkelwar
      Pages 121-140

    3. A Survey on Graph Neural Network Based Video Recommendation System

      • Toshi Rawka, Mahipal Jadeja
      Pages 141-155

    4. Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques

      • Toshita Sharma, Rrubaa Panchendrarajan, Akrati Saxena
      Pages 157-176

    5. Predicting Mental Health and Nutritional Status from Social Media Profile Using Deep Learning

      • Zakir Hussain, Malaya Dutta Borah
      Pages 177-193

    6. Impact of Artificial Intelligence-Based Chatbots on Customer Engagement and Business Growth

      • Chitra Krishnan, Aditya Gupta, Astha Gupta, Gurinder Singh
      Pages 195-210
Back to top

Keywords

About this book

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.

 


Editors and Affiliations

  • Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  • Urban Design and Regional Planning Unit, University of Alicante, Alicante, Spain

    Leticia Serrano-Estrada

  • Dept of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Akrati Saxena

  • Department of Computer Science and Engineering, National Institute Of Technology Silchar, Cachar, India

    Anupam Biswas

Bibliographic Information

  • Book Title: Deep Learning for Social Media Data Analytics

  • Editors: Tzung-Pei Hong, Leticia Serrano-Estrada, Akrati Saxena, Anupam Biswas

  • Series Title: Studies in Big Data

  • DOI: https://doi.org/10.1007/978-3-031-10869-3

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-031-10868-6Published: 19 September 2022

  • Softcover ISBN: 978-3-031-10871-6Published: 20 September 2023

  • eBook ISBN: 978-3-031-10869-3Published: 18 September 2022

  • Series ISSN: 2197-6503

  • Series E-ISSN: 2197-6511

  • Edition Number: 1

  • Number of Pages: X, 299

  • Number of Illustrations: 21 b/w illustrations, 65 illustrations in colour

  • Topics: Data Engineering, Cyber-physical systems, IoT, Computational Intelligence, Big Data, Social Media

Publish with us

Policies and ethics

Back to top

Search

Navigation