04257nam a22005655i 4500 978-3-031-51917-8 DE-He213 20251030112305.0 cr nn 008mamaa 240610s2024 sz | s |||| 0|eng d 9783031519178 978-3-031-51917-8 10.1007/978-3-031-51917-8 doi QA76.9.D343 UNF bicssc UYQE bicssc COM021030 bisacsh UNF thema UYQE thema Qamar, Usman. author. aut http://id.loc.gov/vocabulary/relators/aut 78507 Applied Text Mining [electronic resource] / by Usman Qamar, Muhammad Summair Raza. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXIII, 494 p. 111 illus., 22 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Data mining. 6941 Machine learning. 55059 Natural language processing (Computer science). 65131 Information storage and retrieval systems. 65568 Data Mining and Knowledge Discovery. Machine Learning. 55059 Natural Language Processing (NLP). 66164 Information Storage and Retrieval. 65570 Raza, Muhammad Summair. author. aut http://id.loc.gov/vocabulary/relators/aut 78508 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031519161 Printed edition: 9783031519185 https://doi.org/10.1007/978-3-031-51917-8 ZDB-2-SMA ZDB-2-SXMS 60635 60635