04040nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001600188072001700204072002300221072001500244072001600259100008200275245008900357250001800446264006900464300006800533336002600601337002600627338003600653347002400689505056300713520160301276650002302879650002902902650005902931650005402990650004103044650002903085650004603114650004603160700009203206710003403298773002603332776003603358776003603394856004603430912001403476912001503490999001703505978-3-031-51917-8DE-He21320251030112305.0cr nn 008mamaa240610s2024    sz |    s    |||| 0|eng d  a97830315191789978-3-031-51917-87 a10.1007/978-3-031-51917-82doi 4aQA76.9.D343 7aUNF2bicssc 7aUYQE2bicssc 7aCOM0210302bisacsh 7aUNF2thema 7aUYQE2thema1 aQamar, Usman.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97850710aApplied Text Miningh[electronic resource] /cby Usman Qamar, Muhammad Summair Raza.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXXIII, 494 p. 111 illus., 22 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aPart 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.  aThis 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. 0aData mining.96941 0aMachine learning.955059 0aNatural language processing (Computer science).965131 0aInformation storage and retrieval systems.96556814aData Mining and Knowledge Discovery.24aMachine Learning.95505924aNatural Language Processing (NLP).96616424aInformation Storage and Retrieval.9655701 aRaza, Muhammad Summair.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9785082 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303151916108iPrinted edition:z978303151918540uhttps://doi.org/10.1007/978-3-031-51917-8  aZDB-2-SMA  aZDB-2-SXMS  c60635d60635