Text Data Mining / by Chengqing Zong, Rui Xia, Jiajun Zhang.

Format:

Book

Author/Creator:

Zong, Chengqing, Author.

Xia, Rui., Author.

Zhang, Jiajun, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Natural language processing (Computer science).

Data mining.

Machine learning.

Natural Language Processing (NLP).

Data Mining and Knowledge Discovery.

Machine Learning.

Local Subjects:

Natural Language Processing (NLP).

Data Mining and Knowledge Discovery.

Machine Learning.

Physical Description:

1 online resource (XXI, 351 pages) : 214 illustrations, 7 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

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.

Contents:

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. .

Other Format:

Printed edition:

ISBN:

978-981-16-0100-2

9789811601002

Access Restriction:

Restricted for use by site license.