讲演摘要

Sentiment analysis or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions expressed in written language. It is one of the most active research areas in natural language processing (NLP) due to many challenging research problems and a wide range of practical applications. In this talk, I will start with discussing the mainstream sentiment analysis research and then move on to describe some of our recent work on modeling interactive social media such as debates and discussions, which represent another kind of analysis of sentiment. Here the goal is to mine and summarize disagreement and agreement expressions, points of contention, questions and answers, arguing natures and interactions of participants, and whether the participants exhibit tolerance in their debates/discussions. Tolerance is an important concept in the field of communication, and is a sub-facet of deliberation which refers to critical thinking and exchange of rational arguments on an issue among participants that seek to achieve consensus/solution. This research naturally connects computer science and social science, especially communication and political sciences.

讲演者简介

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. Before joining UIC, he was a faculty member at the National University of Singapore. His current research interests include sentiment analysis and opinion mining, data mining, machine learning, opinion spam detection, and natural language processing (NLP). He has published extensively in highly ranked conferences and journals. He is also the author of two books: “Sentiment Analysis and Opinion Mining” (Morgan and Claypool, 2012) and “Web Data Mining: Exploring Hyperlinks, Contents and Usage Data” (Springer, 2006 and 2nd ed., 2011). Apart from research impacts, his work has also made important social impacts. Some of his work has been widely reported in the popular press, including a front-page article in The New York Times. On professional services, Liu has served as program committee chair of most leading data mining related conferences of ACM, IEEE, and SIAM, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor for several leading data mining journals, including IEEE TKDE, TWEB, and DMKD, and as area/track chairs or senior PC members of numerous data mining, NLP, and Web technology conferences. He currently also 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/.