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Applied text mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied text miningby Usman Qamar, Muhammad Summair Raza.
作者:	Qamar, Usman.
其他作者:	Raza, Muhammad Summair.
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xxiii, 494 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Text data mining.
電子資源:	https://doi.org/10.1007/978-3-031-51917-8
ISBN:	9783031519178$q(electronic bk.)

Applied text mining
Qamar, Usman.

Applied text mining[electronic resource] /by Usman Qamar, Muhammad Summair Raza. - Cham :Springer Nature Switzerland :2024. - xxiii, 494 p. :ill. (some col.), digital ;24 cm.

Part 1: Text Mining Basics -- 1. Introduction to Text Mining -- 2. Text Processing -- 3. Text Mining Applications -- Part 2: Text Analytics -- 4. Feature Engineering for Text Representations -- 5. Text Classification -- 6. Text Clustering -- 7. Text Summarization and Topic Modeling -- 8. Taxonomy Generation and Dynamic Document Organization -- 9. Visualization Approaches -- Part 3: Deep Learning in Text Mining -- 10. Text Mining Through Deep Learning -- 11. Lexical Analysis and Parsing using Deep Learning -- 12. Machine Translation using Deep Learning.

This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP) It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, including models for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.

ISBN: 9783031519178$q(electronic bk.)

Standard No.: 10.1007/978-3-031-51917-8doiSubjects--Topical Terms:

892173
Text data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Applied text mining
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505 0 $a Part 1: Text Mining Basics -- 1. Introduction to Text Mining -- 2. Text Processing -- 3. Text Mining Applications -- Part 2: Text Analytics -- 4. Feature Engineering for Text Representations -- 5. Text Classification -- 6. Text Clustering -- 7. Text Summarization and Topic Modeling -- 8. Taxonomy Generation and Dynamic Document Organization -- 9. Visualization Approaches -- Part 3: Deep Learning in Text Mining -- 10. Text Mining Through Deep Learning -- 11. Lexical Analysis and Parsing using Deep Learning -- 12. Machine Translation using Deep Learning.
520 $a This textbook covers the concepts, theories, and implementations of text mining and natural language processing (NLP) It covers both the theory and the practical implementation, and every concept is explained with simple and easy-to-understand examples. It consists of three parts. In Part 1 which consists of three chapters details about basic concepts and applications of text mining are provided, including eg sentiment analysis and opinion mining. It builds a strong foundation for the reader in order to understand the remaining parts. In the five chapters of Part 2, all the core concepts of text analytics like feature engineering, text classification, text clustering, text summarization, topic mapping, and text visualization are covered. Finally, in Part 3 there are three chapters covering deep-learning-based text mining, which is the dominating method applied to practically all text mining tasks nowadays. Various deep learning approaches to text mining are covered, including models for processing and parsing text, for lexical analysis, and for machine translation. All three parts include large parts of Python code that shows the implementation of the described concepts and approaches. The textbook was specifically written to enable the teaching of both basic and advanced concepts from one single book. The implementation of every text mining task is carefully explained, based Python as the programming language and Spacy and NLTK as Natural Language Processing libraries. The book is suitable for both undergraduate and graduate students in computer science and engineering.
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