Nicht aus der Schweiz? Besuchen Sie lehmanns.de
Applied Text Mining - Usman Qamar, Muhammad Summair Raza

Applied Text Mining

Buch | Softcover
XXIII, 494 Seiten
2024
Springer International Publishing (Verlag)
978-3-031-51916-1 (ISBN)
CHF 112,30 inkl. MwSt
  • Versand in 15-20 Tagen
  • Versandkostenfrei
  • Auch auf Rechnung
  • Artikel merken

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, includingmodels 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.


Usman Qamar has over 15 years of experience in data engineering and decision sciences both in academia and industry. He is currently Tenured Professor of Data Sciences at the National University of Sciences and Technology (NUST) Pakistan and director of Knowledge and Data Science Research Centre, a Centre of Excellence at NUST, Pakistan. He has authored nearly 200 peer-reviewed publications and has also received multiple research awards.

Muhammad Summair Raza is currently associated with the Virtual University of Pakistan as an assistant professor. He has published various papers in international-level journals and conferences with a focus on rough set theory. His research interests include feature selection, rough set theory, trend analysis, software design, software architecture, and non-functional requirements.

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.

Erscheinungsdatum
Zusatzinfo XXIII, 494 p. 111 illus., 22 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 168 x 240 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Schlagworte Data Mining • Deep learning • feature engineering • Neural networks • sentiment analysis • Text Mining • topic modeling
ISBN-10 3-031-51916-7 / 3031519167
ISBN-13 978-3-031-51916-1 / 9783031519161
Zustand Neuware
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
Datenanalyse für Künstliche Intelligenz

von Jürgen Cleve; Uwe Lämmel

Buch | Softcover (2024)
De Gruyter Oldenbourg (Verlag)
CHF 104,90
Auswertung von Daten mit pandas, NumPy und IPython

von Wes McKinney

Buch | Softcover (2023)
O'Reilly (Verlag)
CHF 62,85