Applied Text Mining
Author:   Usman Qamar ,  Muhammad Summair Raza
Publisher:   Springer International Publishing AG
Edition:   2024 ed.
ISBN:  

9783031519161


Pages:   494
Publication Date:   11 June 2024
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Applied Text Mining


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Author:   Usman Qamar ,  Muhammad Summair Raza
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
ISBN:  

9783031519161


ISBN 10:   3031519167
Pages:   494
Publication Date:   11 June 2024
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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Author Information

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.

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