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Text data mining

Zong, Chengqing.

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Text data mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Text data mining/ by Chengqing Zong, Rui Xia, Jiajun Zhang.
作者:	Zong, Chengqing.
其他作者:	Xia, Rui.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xxi, 351 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1. Introduction -- Chapter 2. Data Annotation and Preprocessing -- Chapter 3. Text Representation -- Chapter 4. Text Representation with Pretraining and Fine-tuning -- Chapter 5. Text classification -- Chapter 6. Text Clustering -- Chapter 7. Topic Model -- Chapter 8. Sentiment Analysis and Opinion Mining -- Chapter 9. Topic Detection and Tracking -- Chapter 10. Information Extraction -- Chapter 11. Automatic Text Summarization.
Contained By:	Springer Nature eBook
標題:	Text data mining. -
電子資源:	https://doi.org/10.1007/978-981-16-0100-2
ISBN:	9789811601002

Text data mining
Zong, Chengqing.

Text data mining[electronic resource] /by Chengqing Zong, Rui Xia, Jiajun Zhang. - Singapore :Springer Singapore :2021. - xxi, 351 p. :ill. (some col.), digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Data Annotation and Preprocessing -- Chapter 3. Text Representation -- Chapter 4. Text Representation with Pretraining and Fine-tuning -- Chapter 5. Text classification -- Chapter 6. Text Clustering -- Chapter 7. Topic Model -- Chapter 8. Sentiment Analysis and Opinion Mining -- Chapter 9. Topic Detection and Tracking -- Chapter 10. Information Extraction -- Chapter 11. Automatic Text Summarization.

This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.

ISBN: 9789811601002

Standard No.: 10.1007/978-981-16-0100-2doiSubjects--Topical Terms:

3495453
Text data mining.

LC Class. No.: JA71.5 / .Z65 2021

Dewey Class. No.: 006.312

Text data mining
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505 0 $a Chapter 1. Introduction -- Chapter 2. Data Annotation and Preprocessing -- Chapter 3. Text Representation -- Chapter 4. Text Representation with Pretraining and Fine-tuning -- Chapter 5. Text classification -- Chapter 6. Text Clustering -- Chapter 7. Topic Model -- Chapter 8. Sentiment Analysis and Opinion Mining -- Chapter 9. Topic Detection and Tracking -- Chapter 10. Information Extraction -- Chapter 11. Automatic Text Summarization.
520 $a This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.
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