Visual and text sentiment analysis through hierarchical deep learning networks. (2019)
- Record Type:
- Book
- Title:
- Visual and text sentiment analysis through hierarchical deep learning networks. (2019)
- Main Title:
- Visual and text sentiment analysis through hierarchical deep learning networks
- Further Information:
- Note: Arindam Chaudhuri.
- Authors:
- Chaudhuri, Arindam
- Contents:
- Intro; Preface; Contents; About the Author; List of Figures; List of Tables; Abstract; Synopsis of the Proposed Book; 1 Introduction; 1.1 Need of This Research; 1.1.1 Motivating Factor; 1.2 Contribution; References; 2 Current State of Art; 2.1 Available Technologies; References; 3 Literature Review; References; 4 Experimental Data Utilized; 4.1 Twitter Datasets; 4.2 Instagram Datasets; 4.3 Viber Datasets; 4.4 Snapchat Datasets; References; 5 Visual and Text Sentiment Analysis; Reference; 6 Experimental Setup: Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks 6.1 Deep Learning Networks6.2 Baseline Method Used; 6.3 Gated Feedforward Recurrent Neural Networks; 6.4 Hierarchical Gated Feedback Recurrent Neural Networks: Mathematical Abstraction; 6.4.1 Forward Pass; 6.4.2 Backward Pass; 6.5 Hierarchical Gated Feedback Recurrent Neural Networks for Multimodal Sentiment Analysis; References; 7 Experimental Results; 7.1 Evaluation Metrics; 7.2 Experimental Results with Twitter Datasets; 7.2.1 Textual Sentiment Analysis; 7.2.2 Visual Sentiment Analysis; 7.2.3 Multimodal Sentiment Analysis; 7.2.4 Error Analysis 7.3 Experimental Results with Instagram Datasets7.3.1 Textual Sentiment Analysis; 7.3.2 Visual Sentiment Analysis; 7.3.3 Multimodal Sentiment Analysis; 7.3.4 Error Analysis; 7.4 Experimental Results with Viber Datasets; 7.4.1 Textual Sentiment Analysis; 7.4.2 Visual Sentiment Analysis; 7.4.3 Multimodal Sentiment Analysis; 7.4.4 Error Analysis; 7.5Intro; Preface; Contents; About the Author; List of Figures; List of Tables; Abstract; Synopsis of the Proposed Book; 1 Introduction; 1.1 Need of This Research; 1.1.1 Motivating Factor; 1.2 Contribution; References; 2 Current State of Art; 2.1 Available Technologies; References; 3 Literature Review; References; 4 Experimental Data Utilized; 4.1 Twitter Datasets; 4.2 Instagram Datasets; 4.3 Viber Datasets; 4.4 Snapchat Datasets; References; 5 Visual and Text Sentiment Analysis; Reference; 6 Experimental Setup: Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks 6.1 Deep Learning Networks6.2 Baseline Method Used; 6.3 Gated Feedforward Recurrent Neural Networks; 6.4 Hierarchical Gated Feedback Recurrent Neural Networks: Mathematical Abstraction; 6.4.1 Forward Pass; 6.4.2 Backward Pass; 6.5 Hierarchical Gated Feedback Recurrent Neural Networks for Multimodal Sentiment Analysis; References; 7 Experimental Results; 7.1 Evaluation Metrics; 7.2 Experimental Results with Twitter Datasets; 7.2.1 Textual Sentiment Analysis; 7.2.2 Visual Sentiment Analysis; 7.2.3 Multimodal Sentiment Analysis; 7.2.4 Error Analysis 7.3 Experimental Results with Instagram Datasets7.3.1 Textual Sentiment Analysis; 7.3.2 Visual Sentiment Analysis; 7.3.3 Multimodal Sentiment Analysis; 7.3.4 Error Analysis; 7.4 Experimental Results with Viber Datasets; 7.4.1 Textual Sentiment Analysis; 7.4.2 Visual Sentiment Analysis; 7.4.3 Multimodal Sentiment Analysis; 7.4.4 Error Analysis; 7.5 Experimental Results with Snapchat Datasets; 7.5.1 Textual Sentiment Analysis; 7.5.2 Visual Sentiment Analysis; 7.5.3 Multimodal Sentiment Analysis; 7.5.4 Error Analysis; References; 8 Conclusion; Appendix; Twitter images; Instagram images … (more)
- Publisher Details:
- Singapore : Springer
- Publication Date:
- 2019
- Extent:
- 1 online resource, color illustrations
- Subjects:
- 006.3/12
Natural language processing (Computer science)
Computational linguistics
Public opinion -- Data processing
Data mining
COMPUTERS -- Database Management -- General
COMPUTERS / General
Electronic books
Electronic books - Languages:
- English
- ISBNs:
- 9789811374746
9811374740 - Related ISBNs:
- 9789811374739
9811374732 - Notes:
- Note: Includes bibliographical references and index.
Note: Online resource; title from PDF title page (EBSCO, viewed April 9, 2019). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.410878
- Ingest File:
- 02_510.xml