Five years ago, we authored “Text Mining: Predictive Methods for Analyzing Unstructured Information.” That book was geared mostly to professional practitioners, but was adaptable to course work with some effort by the instructor. Many topics were evolving, and this was one of the earliest efforts to collect material for predictive text mining. Since then, the book has seen extensive use in education, by ourselves and other instructors, with positive responses from students. With more data sourced from the Internet, the field has seen very rapid growth with many new techniques that would interest practitioners. Given the amount of supplementary new material we had begun using, a new edition was clearly needed. A year ago, our publisher asked us to update the book and to add material that would extend its use as a textbook.We have revised many sections, adding new material to reflect the increased use of the web. Exercises and summaries are also provided.
One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents. Developed from the authors’ highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material. Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.