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Multi-modal sentiment analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-modal sentiment analysisby Hua Xu.
作者:	Xu, Hua.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xxi, 261 p. :illustrations, digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Sentiment analysis.
電子資源:	https://doi.org/10.1007/978-981-99-5776-7
ISBN:	9789819957767$q(electronic bk.)

Multi-modal sentiment analysis
Xu, Hua.

Multi-modal sentiment analysis[electronic resource] /by Hua Xu. - Singapore :Springer Nature Singapore :2023. - xxi, 261 p. :illustrations, digital ;24 cm.

Chapter 1. Overview -- Chapter 2. Multimodal Sentiment Analysis Data sets and Preprocessing -- Chapter 3. Early Unimodal Sentiment Analysis of Comment Text based on Traditional Machine Learning -- Chapter 4. Unimodal Sentiment Analysis -- Chapter 5. Cross-Modal Sentiment Analysis -- Chapter 6. Multimodal Sentiment Analysis -- Chapter 7. Multimodal Sentiment Analysis Platform and Application -- Appendix.

The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.

ISBN: 9789819957767$q(electronic bk.)

Standard No.: 10.1007/978-981-99-5776-7doiSubjects--Topical Terms:

892169
Sentiment analysis.

LC Class. No.: QA76.9.S57

Dewey Class. No.: 006.35

Multi-modal sentiment analysis
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505 0 $a Chapter 1. Overview -- Chapter 2. Multimodal Sentiment Analysis Data sets and Preprocessing -- Chapter 3. Early Unimodal Sentiment Analysis of Comment Text based on Traditional Machine Learning -- Chapter 4. Unimodal Sentiment Analysis -- Chapter 5. Cross-Modal Sentiment Analysis -- Chapter 6. Multimodal Sentiment Analysis -- Chapter 7. Multimodal Sentiment Analysis Platform and Application -- Appendix.
520 $a The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.
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