Text Data Management and Analysis

"...advanced undergraduate students might find this book to be a valuable reference for getting acquainted with both information retrieval and text mining in a single volume, a worthwhile achievement for a 500-page textbook." - Fernando Berzal for ACM Computing Reviews

ChengXiang Zhai is a Professor of Computer Science and Willett Faculty Scholar at the University of Illinois at Urbana-Champaign, where he is also affiliated with the Graduate School of Library and Information Science, Institute for Genomic Biology, and Department of Statistics. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and then Senior Research Scientist from 1997-2000. His research interests include information retrieval, text mining, natural language processing, machine learning, biomedical and health informatics, and intelligent education information systems. He has published over 200 research papers in major conferences and journals. He served as an Associate Editor for Information Processing and Management, as an Associate Editor of ACM Transactions on Information Systems, and on the editorial board of Information Retrieval Journal. He was a conference program co-chair of ACM CIKM 2004, NAACL HLT 2007, ACM SIGIR 2009, ECIR 2014, ICTIR 2015, and WWW 2015, and conference general co-chair for ACM CIKM 2016. He is an ACM Distinguished Scientist and a recipient of multiple awards, including the ACM SIGIR 2004 Best Paper Award, the ACM SIGIR 2014 Test of Time Paper Award, Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, Microsoft Beyond Search Research Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Sean Massung is a Ph.D. candidate in computer science at the University of Illinois at Urbana-Champaign, where he also received both his B.S. and M.S. degrees. He is a co-founder of META and uses it in all of his research. He has been instructor for CS 225: Data Structures and Programming Principles, CS 410: Text Information Systems, and CS 591txt: Text Mining Seminar. He is included in the 2014 List of Teachers Ranked as Excellent at the University of Illinois and has received an Outstanding Teaching Assistant Award and CS@Illinois Outstanding Research Project Award. He has given talks at Jump Labs Champaign and at UIUC for Data and Information Systems Seminar, Intro to Big Data, and Teaching Assistant Seminar. His research interests include text mining applications in information retrieval, natural language processing, and education.

PART I. OVERVIEW AND BACKGROUND Introduction Background Text Data Understanding MeTA: A Unified Toolkit for Text Data Management and Analysis PART II. TEXT DATA ACCESS Overview of Text Data Access Retrieval Models Feedback Search Engine Implementation Search Engine Evaluation Web Search Recommender Systems PART III. TEXT DATA ANALYSIS Overview of Text Data Analysis Word Association Mining Text Clustering Text Categorization Text Summarization Topic Analysis Opinion Mining and Sentiment Analysis PART IV. UNIFIED TEXT DATA MANAGEMENT ANALYSIS SYSTEM Toward a Unified System for Text Management and Analysis Appendix A. Bayesian Statistics Appendix B. Expectation-Maximization Appendix C. KL-divergence and Dirichlet Prior Smoothing References Index Authors Biographies