Hong / Serrano-Estrada / Saxena Deep Learning for Social Media Data Analytics

1. Auflage 2022
ISBN: 978-3-031-10869-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 113, 299 Seiten, eBook

Reihe: Studies in Big Data

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.

Zielgruppe

Research

Autoren/Hrsg.

Hong, Tzung-Pei

Serrano-Estrada, Leticia

Saxena, Akrati

Biswas, Anupam

Inhaltsverzeichnis

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.