Text Mining

Ashok N. Srivastava is the Principal Investigator of the Integrated Vehicle Health Management research project in the NASA Aeronautics Research Mission Directorate. Dr. Srivastava also leads the Intelligent Data Understanding group at NASA Ames Research Center.Mehran Sahami is an Associate Professor and Associate Chair for Education in the computer science department at Stanford University.

• Analysis of Text Patterns Using Kernel Methods. Detection of Bias in Media Outlets with Statistical Learning Methods. Collective Classification for Text Classification. Topic Models. Nonnegative Matrix and Tensor Factorization for Discussion Tracking. Text Clustering with Mixture of von Mises–Fisher Distributions. Constrained Partitional Clustering of Text Data: An Overview. Adaptive Information Filtering. Utility-Based Information Distillation. Text Search Enhanced with Types and Entities. Index.

… a very good overview of some state-of-the-art capabilities. … In summary, the book provides several algorithms for text mining classification, clustering, and applications, including both mathematical background and experimental observations. For readers interested in specific areas, there are several useful references. Researchers can use this book to learn more about today's field of text mining.—Computing Reviews, March 2010… Not long ago people were expressing concern about the deluge of information with which we were being faced. Tools such as those described in this book present one way in which we might cope with this deluge. The separate contributions are well written, and there does seem to be a consistency which can only have arisen from sound editorial work … . This would be a perfect volume to give a new Ph.D. student about to start work on statistical and data mining methods of text analysis, and perhaps casting about for a particular area of methodology on which to focus, or for a particular application area to address. It provides a first-class overview of the scope of an area which can only grow in importance in the coming years.—David J. Hand, International Statistical Review, 2010This book is a worthy contribution to the field of text mining. By focusing on classification (rather than exhaustively covering extraction, summarization, and other tasks), it achieves the right balance of coherence and comprehensiveness. It collects papers by the leading authors in the field, who employ and explain a variety of techniques—kernel methods, link analysis, latent Dirichlet allocation, non-negative matrix factorization, and others. Together the papers bring unity and clarity to a disjointed and sometimes perplexing field and serve as the perfect introduction for an advanced student.—Peter Norvig, Director of Research, Google, Inc., Mountain View, California, USAThis is a state-of-the-art, outstanding collection of overviews on text mining by a group of leading researchers in the field. The book meets an imminent need for an up-to-date overview of this exciting, dynamic research frontier and may serve as an excellent textbook on text mining for graduate students and researchers in the field as well.—Jiawei Han, University of Illinois at Urbana-Champaign, USA