Title: Discovering Keywords for Search, Covariate Shift, and Lifelong Learning

by Prof. Bing Liu (University of Illinois at Chicago)

Time: 10:00 -- 11:30 (Friday, 15 January)

Location: Room 106, ICST Building

Abstract: In almost any application of social media content analysis, the user is interested in studying a particular topic. Collecting posts relevant to the topic from a social media data source is a necessary step. Due to the huge size of social media sources (e.g., Twitter, Weibo, and Facebook), the user has to use some keywords to search for relevant posts. However, gathering a set of representative topical keywords is a very tedious and time-consuming task that often involves a lengthy iterative process of searching and manual reading. In this talk, I first discuss this problem and present an algorithm to help the user discover such search keywords. After searching using the keywords, the resulting set of posts can still be quite noisy because many posts containing the keywords may not be relevant. A supervised learning step is needed to filter out those irrelevant posts. Here I discuss a sampling selection bias problem faced by learning, called negative covariate shift, and present an algorithm to deal with it. Finally, I will discuss how lifelong machine learning may be employed to help tackle these problems.

Biography

Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence from the University of Edinburgh. His research interests include sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals. He is one of the pioneer researchers of sentiment analysis and opinion mining, and pioneered the research of fake/deceptive opinion detection. Two of his papers have received 10-year test-of-time awards from KDD, the premier conference of knowledge discovery and data mining. He has also authored three books: two on sentiment analysis and one on Web data mining. Some of his work has been widely reported in the press, including a front-page article in The New York Times. On professional services, Liu has served as program chairs of leading data mining conferences of ACM, IEEE, and SIAM: KDD, ICDM, CIKM, WSDM, and SDM, as associate editors of leading journals such as TKDE, TWEB, DMKD, and as area chairs of numerous NLP, Web technology, and data mining conferences. Currently, he serves as the Chair of ACM SIGKDD. He is an ACM Fellow and an IEEE Fellow. Additional information about him can be found from http://www.cs.uic.edu/~liub/.