The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field
Giving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search.
The book begins with chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use.
There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field.
Analysis of Text Patterns Using Kernel Methods
Marco Turchi, Alessia Mammone, and Nello Cristianini
General Overview on Kernel Methods
Kernels for Text
Conclusion and Further Reading
Detection of Bias in Media Outlets with Statistical Learning Methods
Blaz Fortuna, Carolina Galleguillos, and Nello Cristianini
Overview of the Experiments
Data Collection and Preparation
News Outlet Identification
Topic-Wise Comparison of Term Bias
News Outlets Map
Appendix A: Support Vector Machines
Appendix B: Bag of Words and Vector Space Models
Appendix C: Kernel Canonical Correlation Analysis
Appendix D: Multidimensional Scaling
Collective Classification for Text Classification
Galileo Namata, Prithviraj Sen, Mustafa Bilgic, and Lise Getoor
Collective Classification: Notation and Problem Definition
Approximate Inference Algorithms for Approaches Based on Local Conditional Classifiers
Approximate Inference Algorithms for Approaches Based on Global Formulations
Learning the Classifiers
David M. Blei and John D. Lafferty
Latent Dirichlet Allocation (LDA)
Posterior Inference for LDA
Dynamic Topic Models and Correlated Topic Models
Nonnegative Matrix and Tensor Factorization for Discussion Tracking
Brett W. Bader, Michael W. Berry, and Amy N. Langville
Tensor Decompositions and Algorithms
Observations and Results
Visualizing Results of the NMF Clustering
Text Clustering with Mixture of von Mises–Fisher Distributions
Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, and Suvrit Sra
EM on a Mixture of vMFs (moVMF)
Handling High-Dimensional Text Datasets
Conclusions and Future Work
Constrained Partitional Clustering of Text Data: An Overview
Sugato Basu and Ian Davidson
Uses of Constraints
Partitional Clustering with Constraints
Learning Distance Function with Constraints
Satisfying Constraints and Learning Distance Functions
Adaptive Information Filtering
Standard Evaluation Measures
Standard Retrieval Models and Filtering Approaches
Collaborative Adaptive Filtering
Novelty and Redundancy Detection
Other Adaptive Filtering Topics
Utility-Based Information Distillation
Yiming Yang and Abhimanyu Lad
A Sample Task
Experiments and Results
Text Search Enhanced with Types and Entities
Soumen Chakrabarti, Sujatha Das, Vijay Krishnan, and Kriti Puniyani
Entity-Aware Search Architecture
Understanding the Question
Scoring Potential Answer Snippets
Indexing and Query Processing
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.
… 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, 2010
This 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, USA
This 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