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Fundamentals of Predictive Text Mining

  • Textbook
  • © 2015

Overview

Authors:
  1. Sholom M. Weiss

    1. Deaprtment of computer science, Rutgers University, Piscataway, USA

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  2. Nitin Indurkhya

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

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    You can also search for this author in PubMed   Google Scholar

  3. Tong Zhang

    1. Department of Statistics, Hill Center, Rutgers University, Piscataway, USA

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    You can also search for this author in PubMed   Google Scholar

  • 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
  • Includes supplementary material: sn.pub/extras
  • Request lecturer material: sn.pub/lecturer-material

Part of the book series: Texts in Computer Science (TCS)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiii
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  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
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Keywords

About this book

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.

Reviews

“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)

Authors and Affiliations

  • 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

About the authors

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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