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Can lend chapters, not whole ebooks
Title
Text data mining / Chengqing Zong, Rui Xia, Jiajun Zhang.
ISBN
9789811601002 (electronic bk.)
9811601003 (electronic bk.)
9789811601019 (print)
9811601011
9789811601026 (print)
981160102X
9789811600999
9811600996
Published
Singapore : Springer, [2021]
Language
English
Description
1 online resource : illustrations (some color)
Item Number
10.1007/978-981-16-0100-2 doi
Call Number
JA71.5
Dewey Decimal Classification
006.3/12
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (EBSCO, viewed June 2, 2021).
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.
