Name: Text Mining and Analytics
Explore algorithms for mining and analyzing big text data to discover interesting patterns, extract useful knowledge, and support decision making.
This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the “shallow” but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
This course will be covering the following topics:
Overview of text mining and analytics
Natural language processing and text representation
Word association mining
Topic mining and analysis with statistical topic models
Text clustering and categorization
Opinion mining and sentiment analysis
Integrative analysis of text and structured data
About the Instructors
Department of Computer Science
University of Illinois at Urbana-Champaign
ChengXiang Zhai is a Professor of Computer Science at the University of Illinois at Urbana-Champaign, where he also holds a joint appointment at the Institute for Genomic Biology, Statistics, and the Graduate School of Library and Information Science. His research interests include information retrieval, text mining, natural language processing, machine learning, and bioinformatics, and has published over 200 papers in these areas with an H-index of 58 in Google Scholar. He is an Associate Editor of ACM Transactions on Information Systems, and Information Processing and Management, and the Americas Editor of Springer’s Information Retrieval Book Series. He is 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 best paper awards, Rose Award for Teaching Excellence at UIUC, Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).