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Springer Deep Learning for Social Media Data Analytics - Springer

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Description

Deep Learning for Social Media Data Analytics is a comprehensive guide from Springer's Studies in Big Data series that explores advanced techniques for extracting insights from social media data. This volume combines theoretical foundations with practical applications of deep learning methods, making it essential reading for data scientists, researchers, and analysts working with large-scale social media datasets. The book addresses the unique challenges of analysing unstructured social data, including text, images, and user behaviour patterns, using state-of-the-art deep learning architectures. Whether you're developing recommendation systems, performing sentiment analysis, or building predictive models from social platforms, this resource provides the methodologies and case studies needed to implement effective solutions. Part of Springer's respected Studies in Big Data series, it bridges the gap between theoretical machine learning concepts and real-world social media applications.

Key Features

Part of Springer's Studies in Big Data series, volume 113, providing peer-reviewed research and established academic credibility.

Covers deep learning architectures and methods specifically designed for processing social media text, images, and user interaction data.

Includes practical techniques for sentiment analysis, user profiling, and predictive modelling on social platforms.

Combines theoretical foundations with real-world case studies demonstrating application of deep learning to social analytics challenges.

Addresses scalability and performance optimisation for handling large-scale social media datasets.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
20 September 2023
Listed Since
23 August 2023

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