Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

• Κύριος συγγραφέας: Chaudhuri, Arindam (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: SpringerBriefs in Computer Science,
• Θέματα: Information storage and retrieval.; Database management.; Data mining.; Pattern recognition.; Information Storage and Retrieval.; Database Management.; Data Mining and Knowledge Discovery.; Pattern Recognition.

This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). S...

Πίνακας περιεχομένων:

• Chapter1. Introduction
• Chapter 2. Current State of Art
• Chapter 3. Literature Review
• Chapter 4. Twitter Datasets Used
• Chapter 5. Visual and Text Sentiment Analysis
• Chapter 6. Experimental Setup: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks
• Chapter 7. Twitter Datasets Used
• Chapter 8. Experimental Results
• Chapter 9. Conclusion.