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Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks b

grandeagleretail
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eBay item number:386717610816
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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
ISBN-13
9789811374739
Book Title
Visual and Text Sentiment Analysis through Hierarchical Deep Lear
ISBN
9789811374739
Subject Area
Computers
Publication Name
Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks
Publisher
Springer
Item Length
9.3 in
Subject
System Administration / Storage & Retrieval, Databases / Data Mining, Databases / General
Publication Year
2019
Series
Springerbriefs in Computer Science Ser.
Type
Textbook
Format
Trade Paperback
Language
English
Author
Arindam Chaudhuri
Item Weight
16 Oz
Item Width
6.1 in
Number of Pages
Xix, 98 Pages

About this product

Product Identifiers

Publisher
Springer
ISBN-10
9811374732
ISBN-13
9789811374739
eBay Product ID (ePID)
9038412778

Product Key Features

Number of Pages
Xix, 98 Pages
Publication Name
Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks
Language
English
Publication Year
2019
Subject
System Administration / Storage & Retrieval, Databases / Data Mining, Databases / General
Type
Textbook
Author
Arindam Chaudhuri
Subject Area
Computers
Series
Springerbriefs in Computer Science Ser.
Format
Trade Paperback

Dimensions

Item Weight
16 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Reviews
"Readers interested in sentiment analysis research will find it useful. The research is a good contribution to our understanding of HGFRNNs and the development of a technique for sentiment analysis." (Maulik A. Dave, Computing Reviews, January 25, 2021)
CLASSIFICATION_METADATA
{"IsNonfiction":["No"],"IsOther":["Yes"],"IsAdult":["No"],"MuzeFormatDesc":["Trade Paperback"],"IsChildren":["No"],"Genre":["COMPUTERS"],"Topic":["Databases / Data Mining","System Administration / Storage & Retrieval","Databases / General"],"IsTextBook":["No"],"IsFiction":["No"]}
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
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.
Synopsis
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., 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). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book's novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
LC Classification Number
QA75.5-76.95
ebay_catalog_id
4
Copyright Date
2019

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