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Computing That Serves

Sentiment Analysis and Lifelong Machine Learning

Date: 

Thursday, December 3, 2015 - 11:00am

Speaker: 

Bing Liu

Host: 

Christophe Giraud-Carrier

Sentiment Analysis and Lifelong Machine Learning.
Thursday, December 3, 2015
11:00am  1170 TMCB

Abstract:
Sentiment analysis (SA) or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions. Due to almost unlimited applications and numerous research challenges, SA has been a very active research area in natural language processing (NLP) and data mining. SA is a semantic analysis problem, but is highly targeted and bounded because a SA system does not need to fully “understand” a sentence or document. It only needs to comprehend some aspects of its meaning, e.g., sentiments/opinions and targets. General NL understanding is still far from us, but we may be able to solve the SA problem satisfactorily. In this talk, I will first introduce SA and the existing research, and then go into detail to discuss some of our recent work on lifelong machine learning (LML) and its application to SA. We will see that LML may hold the key to solving the SA problem.

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 test-of-time awards from KDD, the premier conference of data mining. He also authored three books: two on sentiment analysis and one on Web data mining. Some of his work has also 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, and is an IEEE Fellow. Additional information about him can be found from http://www.cs.uic.edu/~liub/.




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