Bing Liu

Professor Department of Computer Science University of Illinois at Chicago (UIC) 851 S. Morgan Street (M/C 152) Chicago, IL 60607-7053

Tel: 1 (312) 685-2570 (Google Voice) Fax: 1 (312) 413 0024

Search Engine Evaluation Google vs. Yahoo vs. Bing 2006 vs. 2007 You can join the evaluation

Textbook Web Data Mining: Exploring Hyperlinks, Contents and Usage Data, 532 pages Order the book: Amazon.com, Springer, or Barnes&Noble.

Recent Talks

• Opinion Mining and Sentiment Analysis: NLP Meets Social Sciences . Talk given at the Invited Workshop on Social Theory and Social Computing, Honolulu, Hawaii, May 22-23, 2010. (Sponsored by the Air Force Office of Scientific Research).
• Sentiment Analysis: A multifaceted Problem. Talk given at the Institute of Automation, Chinese Academy of Sciences, May 11, 2010.
• Learning from Positive and Unlabeled Examples. Talk given at the Institute of Automation, Chinese Academy of Sciences, May 11, 2010.
• PU Learning and Similarity-Based Ranking. Talk given at the Institute of Automation, Chinese Academy of Sciences, May 13, 2010.
• Opinion Mining and Sumamrization. Invited talk given at the MAVIR symposium, Nov 19, 2009, Madrid, Spain.
• Opinion Mining and Sentiment Analysis via Divide and Conquer. Invited Talk given at the Workshop on Mining User-Generated Content, Aug 8, 2009, Singapore Management University, Singapore.
• Sentiment Analysis. Keynote talk at the 5th Annual Text Analytics Summit, Boston, USA, June 1-2, 2009.
• An Interview on Sentiment Analysis and Opinion Mining by textAnalyticsNews.com, April 20, 2009.
• Exploring Users Opinion in Recommender Systems. Invited talk at the KDD-2008 Workshop on Large Scale Recommender Systems, Aug 24, 2008, Las Vegas, USA.
• Opinion Mining and Summarization. Panel talk at Social Media Research Summit @ the 2008 Microsoft Research Faculty Summit, July 30, 2008, Seattle, USA.
• Opinion Mining and Summarization (including review spam detection), tutorial given at WWW-2008, April 21, 2008 in Beijing, China.
• Web Data Extraction and Opinion Mining, invited talk at the Research and Development Dept. of Thomson, Minneapolis, Minnesota, July 26, 2007
• From Web Content Mining to Natural Language Processing, tutorial given at ACL-2007, Prague, Czech Republic, June 23-30 2007.
  • There is also a video made by Reinhard Rapp. It will be posted soon.
• From Topic Search to Opinion Search. Invited talk at IBM T. J. Watson Research Center, New York, June 4, 2007.
• (1) Web Data Extraction and Template Detection, and (2) Sentiment Analysis. Invited talks at Yandex (the most popular search engine in Russia), Moscow, Russia, May 15, 2007.
• Mining Opinions in the User Generated Content on the Web, Invited talk at Motorola Labs, Mar 5 2007. (Similar to the talk given at Google).
• ACM SIGKDD Inaugural Webcast: Web Content Mining, Nov 29, 2006. See live recording, references and other information.
• Mining and Summarizing Opinions on the Web. Invited talk at ACL-06 Workshop on Sentiment and Subjectivity in Text. July 22, 2006, Sydney, Australia.
• Searching Opinions in User-Generated Contents. Invited talk at the Sixth Annual Emerging Information Technology Conference (EITC-06), Aug 10-12, 2006, Dallas, Texas.
• Opinion Mining and Search, Invited talk at Google, Pittsburgh, Sept 29, 2006.
• Web Content Mining: tutorial given at WWW-2005 and WISE-2005.

Research Projects

• Web Mining, Text Mining, and Sentiment Analysis
  • Opinion Mining, Sentiment Analysis and Opinion Spam Detection
  • Web Data Extraction from Flat and Nested Data Records - automated wrapper generation
    • Download MDR; Two new systems are DEPTA and NET.
  • Information Synthesis (or Knowledge Synthesis): Mining a "Book" on the Web given a topic
  • Web page template detection and noise removal - page segmentation and cleaning
• Learning from Positive and Unlabeled Examples (no labeled negative examples)
• Download our LPU system - LPU and Spy-EM (S-EM) system - S-EM
• Building, Analyzing and Managing a Huge Number of Data Mining Models: To appear in Communications of ACM, 2008.
• Diagnostic Data Mining - Interestingness of Knowledge: Finding interesting and actionable knowledge (a patent has been filed).
  • This work is based on two key ideas, rule cubes and general impressions. It is in the area of interestingness in data mining, i.e., finding interesting and actionable knowledge. We have built a system, called Opportunity Map (also known as OM in Motorola), which has been deployed and in daily use in both the Motorola Global Headquarter (near Chicago) and Motorola China. The system is also commercially available. Motorola has sold copies to Telcommunications companies.
• Data Mining and Knowledge Discovery
  • Classification Based on Associations - Integrating Classification and Association Rule Mining
    • Download CBA - asociation rules based classification
    • I recommend that you use the CBA implementation of Frans Coenen from the Univesity of Livepool. Our version was too old. There is an issue with discretization, which we have no time/people to fix.
    • KDD-98 paper and others (KDD-99, PKDD-00, etc)
  • Association rule mining with multiple minimum supports
    • Important for skewed data. Most real life data are skewed or highly imbalanced.
  • Interestingness: subjective interestingness, rule summary, rule query language, change mining

Teaching

• Fall 2010: CS 583 - Data Mining and Text Mining
• Spring 2010: CS 583 - Data Mining and Text Mining
• Past teaching

Professional Memberships

• Member, Association for Computing Machinery (ACM).
• Member, ACM Special Interest Group on Knowledge Discovery in Data and Data Mining (SIGKDD).
• Member, Institute of Electrical and Electronics Engineers (IEEE).
• Member, American Association of Artificial Intelligence (AAAI).