東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Textual emotion classification using...

Peng, Sancheng.

Linked to FindBook

Google Book

Amazon

博客來

Textual emotion classification using deep broad learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Textual emotion classification using deep broad learning/ by Sancheng Peng, Lihong Cao.
Author:	Peng, Sancheng.
other author:	Cao, Lihong.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xv, 155 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Preface -- Acknowledgements -- Chapter 1. Introduction -- Chapter 2. BERT and Broad Learning for Textual Emotion Classification -- Chapter 3. Cascading Broad Learning for Textual Emotion Classification -- Chapter 4. Dual Broad Learning for Textual Emotion Classification -- Chapter 5. Single-source Domain Adaptation for Emotion Classification Using CNN-Based Broad Learning -- Chapter 6. Multi-source Domain Adaptation for Emotion Classification Using Bi-LSTM-Based Broad Learning. Chapter 7. Emotion Classification in Textual Conversations Using Deep Broad Learning -- Chapter 8. Rational Graph Attention Network and Broad Learning for Emotion Classification in Textual Conversations -- Chapter 9. Summary and Outlook.
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-031-67718-2
ISBN:	9783031677182

Textual emotion classification using deep broad learning
Peng, Sancheng.

Textual emotion classification using deep broad learning[electronic resource] /by Sancheng Peng, Lihong Cao. - Cham :Springer Nature Switzerland :2024. - xv, 155 p. :ill. (some col.), digital ;24 cm. - Socio-affective computing,v. 112509-5714 ;. - Socio-affective computing ;v. 11..

Preface -- Acknowledgements -- Chapter 1. Introduction -- Chapter 2. BERT and Broad Learning for Textual Emotion Classification -- Chapter 3. Cascading Broad Learning for Textual Emotion Classification -- Chapter 4. Dual Broad Learning for Textual Emotion Classification -- Chapter 5. Single-source Domain Adaptation for Emotion Classification Using CNN-Based Broad Learning -- Chapter 6. Multi-source Domain Adaptation for Emotion Classification Using Bi-LSTM-Based Broad Learning. Chapter 7. Emotion Classification in Textual Conversations Using Deep Broad Learning -- Chapter 8. Rational Graph Attention Network and Broad Learning for Emotion Classification in Textual Conversations -- Chapter 9. Summary and Outlook.

In this book, the authors systematically and comprehensively discuss textual emotion classification by using deep broad learning. Since broad learning possesses certain advantages such as simple network structure, short training time and strong generalization ability, it is a new and promising framework for textual emotion classification in artificial intelligence. As a result, how to combine deep and broad learning has become a new trend of textual emotion classification, a booming topic in both academia and industry. For a better understanding, both quantitative and qualitative results are present in figures, tables, or other suitable formats to give the readers the broad picture of this topic along with unique insights of common sense and technical details, and to pave a solid ground for their forthcoming research or industry applications. In a progressive manner, the readers will gain exclusive knowledge in textual emotion classification using deep broad learning and be inspired to further investigate this underexplored domain. With no other similar book existing in the literature, the authors aim to make the book self-contained for newcomers, only a few prerequisites being expected from the readers. The book is meant as a reference for senior undergraduates, postgraduates, scientists and researchers interested to have a quick idea of the foundations and research progress of security and privacy in federated learning, and it can equally well be used as a textbook by lecturers, tutors, and undergraduates.

ISBN: 9783031677182

Standard No.: 10.1007/978-3-031-67718-2doiSubjects--Topical Terms:

565309
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Textual emotion classification using deep broad learning
LDR:03321nmm a2200337 a 4500 001 2374740
003 DE-He213
005 20240928131746.0
006 m d
007 cr nn 008maaau
008 241231s2024 sz s 0 eng d
020 $a 9783031677182 $q (electronic bk.)
020 $a 9783031677175 $q (paper)
024 7 $a 10.1007/978-3-031-67718-2 $2 doi
035 $a 978-3-031-67718-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.N38
072 7 $a UYQL $2 bicssc
072 7 $a COM073000 $2 bisacsh
072 7 $a UYQL $2 thema
082 0 4 $a 006.35 $2 23
090 $a QA76.9.N38 $b P398 2024
100 1 $a Peng, Sancheng. $3 3723738
245 1 0 $a Textual emotion classification using deep broad learning $h [electronic resource] / $c by Sancheng Peng, Lihong Cao.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xv, 155 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Socio-affective computing, $x 2509-5714 ; $v v. 11
505 0 $a Preface -- Acknowledgements -- Chapter 1. Introduction -- Chapter 2. BERT and Broad Learning for Textual Emotion Classification -- Chapter 3. Cascading Broad Learning for Textual Emotion Classification -- Chapter 4. Dual Broad Learning for Textual Emotion Classification -- Chapter 5. Single-source Domain Adaptation for Emotion Classification Using CNN-Based Broad Learning -- Chapter 6. Multi-source Domain Adaptation for Emotion Classification Using Bi-LSTM-Based Broad Learning. Chapter 7. Emotion Classification in Textual Conversations Using Deep Broad Learning -- Chapter 8. Rational Graph Attention Network and Broad Learning for Emotion Classification in Textual Conversations -- Chapter 9. Summary and Outlook.
520 $a In this book, the authors systematically and comprehensively discuss textual emotion classification by using deep broad learning. Since broad learning possesses certain advantages such as simple network structure, short training time and strong generalization ability, it is a new and promising framework for textual emotion classification in artificial intelligence. As a result, how to combine deep and broad learning has become a new trend of textual emotion classification, a booming topic in both academia and industry. For a better understanding, both quantitative and qualitative results are present in figures, tables, or other suitable formats to give the readers the broad picture of this topic along with unique insights of common sense and technical details, and to pave a solid ground for their forthcoming research or industry applications. In a progressive manner, the readers will gain exclusive knowledge in textual emotion classification using deep broad learning and be inspired to further investigate this underexplored domain. With no other similar book existing in the literature, the authors aim to make the book self-contained for newcomers, only a few prerequisites being expected from the readers. The book is meant as a reference for senior undergraduates, postgraduates, scientists and researchers interested to have a quick idea of the foundations and research progress of security and privacy in federated learning, and it can equally well be used as a textbook by lecturers, tutors, and undergraduates.
650 0 $a Natural language processing (Computer science) $3 565309
650 0 $a Deep learning (Machine learning) $3 3538509
650 1 4 $a Natural Language Processing (NLP) $3 3381674
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Linguistics. $3 893900
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Cao, Lihong. $3 3723739
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Socio-affective computing ; $v v. 11. $3 3723740
856 4 0 $u https://doi.org/10.1007/978-3-031-67718-2
950 $a Biomedical and Life Sciences (SpringerNature-11642)