Bing Liu (computer scientist)
Bing Liu (born 1963) is a Chinese-American professor of computer science who specialized in data mining, machine learning, and natural language processing. In 2002, he became a scholar at University of Illinois at Chicago.[1] He holds a PhD from the University of Edinburgh.[2]
Academic research
He developed a mathematical model which can reveal fake advertising.[3] Also he teaches the course "Data Mining" during the Fall and Spring semesters at UIC. The course usually involves a project and various quiz/examinations as grading criteria.
He is best known for his research on sentiment analysis (also called opinion mining), fake/deceptive opinion detection, and using association rules for prediction. He also made important contributions to learning from positive and unlabeled examples (or PU learning), Web data extraction, and interestingness in data mining.
Two of his research papers published in KDD-1998 and KDD-2004 received KDD Test-of-Time awards in 2014 and 2015. In 2013, he was elected chair of SIGKDD, ACM Special Interest Group on Knowledge Discovery and Data Mining.
Research on Association Rules For Prediction
Association rule-based classification takes into account the relationships between each and all items in a dataset and the class into which one is trying to classify that item. The basis is that there are two classes, a positive class and a negative class, into which one classifies items. Some classification algorithms only check if a case/item is in the positive class, without understanding how much exactly the probability of it being in that class is.[4] Liu and his collaborators described a new association rule-based classification algorithm that takes into account the relationship between items and the positive and negative classes.[4] Each item is given a probability or scoring of being in the positive class or the negative class. It then ranks the items as per which ones would be most likely to be in the positive class.[4]
Research on Sentiment Analysis
In a paper that Liu collaborated on, the authors studied the relationship between opinion lexicons and opinion targets. Opinion lexicons are word sets and opinion targets are topics on which there is an opinion. The authors of that paper discuss how their algorithm uses a limited opinion word set with the topic and through double propagation, one is able to form a more detailed opinion word set on a set of sentences.
Honors and awards
- In 2014, he was named Fellow of IEEE (Institute of Electrical and Electronics Engineers).
- In 2015, he was named Fellow of ACM "For contributions to knowledge discovery and data mining, opinion mining, and sentiment analysis". [5]
- In 2016, he was elected Fellow of AAAI "For significant contributions to data mining and development of widely used sentiment analysis, opinion spam detection, and Web mining algorithms." [6]
Publications
Articles
- 2008. (with Ross Quinlan, Qiang Yang, Philip S. Yu, Zhou Zhihua, and David Hand et al.). Top 10 algorithms in data mining. Knowledge and Information Systems 14.1: 1-37.
References
- ^ Christy Levy (February 19, 2013). "On the internet, no one knows you're lying". Retrieved January 1, 2015.
- ^ https://www.cs.uic.edu/~liub/Bing-Liu-short-CV.html
- ^ David Streitfield (January 26, 2012). "For $2 a Star, an Online Retailer Gets 5-Star Product Reviews". The New York Times.
- ^ a b c Liu, Bing; Ma, Yiming; Wong, Ching Kian; Yu, Philip S. (2003-03-01). "Scoring the Data Using Association Rules". Applied Intelligence. 18 (2): 119–135. doi:10.1023/A:1021931008240. ISSN 1573-7497.
- ^ "ACM Fellows Named for Computing Innovations that Are Advancing Technology in the Digital Age". ACM. 8 December 2015. Archived from the original on 9 December 2015. Retrieved 9 December 2015.
- ^ "AAAI Fellows Elected in 2016". AAAI. 2016. Retrieved 2 February 2016.
External links
- Bing Liu publications indexed by Google Scholar
- Official website
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