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Deep Learning for Social Media Data Analytics 2022 ed. [Pehme köide]

Edited by , Edited by , Edited by , Edited by
  • Formaat: Paperback / softback, 299 pages, kõrgus x laius: 235x155 mm, kaal: 480 g, 65 Illustrations, color; 21 Illustrations, black and white; X, 299 p. 86 illus., 65 illus. in color., 1 Paperback / softback
  • Sari: Studies in Big Data 113
  • Ilmumisaeg: 20-Sep-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303110871X
  • ISBN-13: 9783031108716
Teised raamatud teemal:
  • Pehme köide
  • Hind: 141,35 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 166,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
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  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 299 pages, kõrgus x laius: 235x155 mm, kaal: 480 g, 65 Illustrations, color; 21 Illustrations, black and white; X, 299 p. 86 illus., 65 illus. in color., 1 Paperback / softback
  • Sari: Studies in Big Data 113
  • Ilmumisaeg: 20-Sep-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 303110871X
  • ISBN-13: 9783031108716
Teised raamatud teemal:
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.





 
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.