عنوان

Visual and text sentiment analysis through hierarchical deep learning networks /

شابک

9789811374746

شابک

9789811374753

شابک

9811374740

شابک

9811374759

شابک اشتباه

9789811374739

شابک اشتباه

9811374732

عنوان اصلي

Visual and text sentiment analysis through hierarchical deep learning networks /

نام عام مواد

[Book]

نام نخستين پديدآور

Arindam Chaudhuri.

محل نشرو پخش و غیره

Singapore :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2019.

نام خاص و کميت اثر

1 online resource :

ساير جزييات

color illustrations

عنوان فروست

SpringerBriefs in Computer Science

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

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

متن يادداشت

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

متن يادداشت

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

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

متن يادداشت

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. --

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9789811374746

عنوان

Visual and text sentiment analysis through hierarchical deep learning networks.

شماره استاندارد بين المللي کتاب و موسيقي

9789811374739

موضوع مستند نشده

Computational linguistics.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Natural language processing (Computer science)

موضوع مستند نشده

Public opinion-- Data processing.

موضوع مستند نشده

Computational linguistics.

موضوع مستند نشده

COMPUTERS-- Database Management-- General.

موضوع مستند نشده

COMPUTERS-- General.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Natural language processing (Computer science)

موضوع مستند نشده

Public opinion-- Data processing.

موضوع مستند نشده

COM-- 000000

موضوع مستند نشده

UND

موضوع مستند نشده

UNH

موضوع مستند نشده

UNH

شماره

ويراست

شماره رده

شماره رده

کد سيستم

مستند نام اشخاص تاييد نشده

Chaudhuri, Arindam

تاريخ عمليات

20200823230707.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y