Text Data Management and Analysis

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.

This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

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.