Fundamentals of Predictive Text Mining

1. Front Matter

   Pages i-xiii

   PDF

2. Overview of Text Mining

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 1-12

3. From Textual Information to Numerical Vectors

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 13-39

4. Using Text for Prediction

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 41-79

5. Information Retrieval and Text Mining

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 81-96

6. Finding Structure in a Document Collection

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 97-118

7. Looking for Information in Documents

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 119-145

8. Data Sources for Prediction: Databases, Hybrid Data and the Web

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 147-164

9. Case Studies

   • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

   Pages 165-201

10. Emerging Directions

    • Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

    Pages 203-222

11. Back Matter

    Pages 223-239

    PDF

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.

• Document Classification
• Information Extraction
• Information Retrieval
• Machine Learning
• Text Mining

“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)

• Deaprtment of computer science, Rutgers University, Piscataway, USA

  Sholom M. Weiss

• School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

  Nitin Indurkhya

• Department of Statistics, Hill Center, Rutgers University, Piscataway, USA

  Tong Zhang

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.

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