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He is best known for his research on [[sentiment analysis]] (also called opinion mining), fake/deceptive opinion detection, and using [[Associative classifier|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.
He is best known for his research on [[sentiment analysis]] (also called opinion mining), fake/deceptive opinion detection, and using [[Associative classifier|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.

=== Research on Association Rules For Prediction ===
[[Associative classifier|Association rule-based classification]] takes into account the relationships between each and all items in a [[Data set|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.


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.
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.

Revision as of 04:16, 25 April 2021

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.

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.

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.

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". [4]
  • 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." [5]

Publications

Articles

References

  1. ^ Christy Levy (February 19, 2013). "On the internet, no one knows you're lying". Retrieved January 1, 2015.
  2. ^ https://www.cs.uic.edu/~liub/Bing-Liu-short-CV.html
  3. ^ David Streitfield (January 26, 2012). "For $2 a Star, an Online Retailer Gets 5-Star Product Reviews". The New York Times.
  4. ^ "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.
  5. ^ "AAAI Fellows Elected in 2016". AAAI. 2016. Retrieved 2 February 2016.