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Textual emotion classification using...

Peng, Sancheng.

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Textual emotion classification using deep broad learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Textual emotion classification using deep broad learning/ by Sancheng Peng, Lihong Cao.
作者:	Peng, Sancheng.
其他作者:	Cao, Lihong.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xv, 155 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Natural language processing (Computer science) -
電子資源:	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

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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.
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