Fundamentals of Predictive Text Mining
Authors: Weiss, Sholom M., Indurkhya, Nitin, Zhang, Tong
Free Preview- Presents a comprehensive, practical and easy-to-read introduction to text mining
- Updated and expanded with new content on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation
- Includes chapter summaries, classroom-tested exercises, and several descriptive case studies
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- About this Textbook
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This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.
- About the authors
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Dr. Sholom M. Weiss is a Professor Emeritus of Computer Science at Rutgers University, a Fellow of the Association for the Advancement of Artificial Intelligence, and co-founder of AI Data-Miner LLC, New York.
Dr. Nitin Indurkhya is faculty member at the School of Computer Science and Engineering, University of New South Wales, Australia, and the Institute of Statistical Education, Arlington, VA, USA. He is also a co-founder of AI Data-Miner LLC, New York.
Dr. Tong Zhang is a Professor of Statistics and Biostatistics at Rutgers University.
- Reviews
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“Fundamentals of predictive text mining is a second edition that is designed as a textbook, with questions and exercises in each chapter. … The book can be used with data mining software for hands-on experience for students. … The book will be very useful for people planning to go into this field or to learn techniques that could be used in a big data environment.” (S. Srinivasan, Computing Reviews, February, 2016)
- Table of contents (9 chapters)
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Overview of Text Mining
Pages 1-12
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From Textual Information to Numerical Vectors
Pages 13-39
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Using Text for Prediction
Pages 41-79
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Information Retrieval and Text Mining
Pages 81-96
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Finding Structure in a Document Collection
Pages 97-118
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Table of contents (9 chapters)
- Download Preface 1 PDF (55.4 KB)
- Download Sample pages 2 PDF (1.2 MB)
- Download Table of contents PDF (189.9 KB)
- Teaching Aids
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Fundamentals of Predictive Text Mining
- Authors
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- Sholom M. Weiss
- Nitin Indurkhya
- Tong Zhang
- Series Title
- Texts in Computer Science
- Copyright
- 2015
- Publisher
- Springer-Verlag London
- Copyright Holder
- Springer-Verlag London
- eBook ISBN
- 978-1-4471-6750-1
- DOI
- 10.1007/978-1-4471-6750-1
- Hardcover ISBN
- 978-1-4471-6749-5
- Softcover ISBN
- 978-1-4471-7113-3
- Series ISSN
- 1868-0941
- Edition Number
- 2
- Number of Pages
- XIII, 239
- Number of Illustrations
- 115 b/w illustrations
- Topics
