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) 355 1318 Fax: 1 (312) 413 0024

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

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

Recent Talks

• 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 Content Mining, Text Mining, and Search
  • Opinion Extraction, Opinion Mining, Comparison Extraction, and Sentiment Analysis
  • 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/compiling a "book" on the Web.
  • 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, 2007.
• Diagnostic Data Mining: Finding interesting and actionable knowledge - a patent has been filed by Motorola
  • 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 our system (CBA - Asociation rules based classification)
    • 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

• Spring 2008: CS 583 - Data Mining and Text Mining
• Fall 2007: CS 583 - Data Mining and Text Mining
• Spring 2007: 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).