Bing Liu							Professional Recognitions
Distinguished Professor Peter L. and Deborah K. Wexler Professor of Computing Department of Computer Science University of Illinois at Chicago (UIC) 851 S. Morgan Street (M/C 152) Chicago, IL 60607-7053					Email Addresses Press Coverage of My Work		ACM SIGKDD Innovation Award 4 Test-of-Time [3 awards + 1 honorable mention] IEEE Fellow AAAI Fellow ACM Fellow

Published Books

Lifelong and Continual Learning Dialogue Systems New Book Springer, February 2024. Order from MIT Press Bookstore Order from Amazon.com

Lifelong Machine Learning (Second Edition) Download for free, if your institution is on this list Get the first edition Morgan & Claypool, 207 pages, August, 2018. Order from Amazon.com

Sentiment Analysis: mining opinions, sentiments, and emotions How to build a Sentiment Analysis system, and more Table of Contents, 448 pages Cambridge Univ. Press, 2020. Order from Amazon.com

Sentiment Analysis and Opinion Mining A comprehensive introduction and survey Morgan & Claypool, May 2012, 167 pages Order from Amazon.com

Web Data Mining: exploring hyperlinks, contents & usage data 2nd Edition, 2011, 622 Pages Order from Amazon.com, Order from Springer Get the eBook

Research Projects

• Opinion Mining, Sentiment Analysis and Opinion Spam Detection
  • Book: Sentiment Analysis and Opinion Mining: Introduction and survey
  • Emotional chatbot - enable chatbots or dialogue systems to converse with emotions
  • Reviews Reading Comprehension: Answer questions using reviews - When you plan to buy a product, do you have questions? Yes, 100%.
• Lifelong Machine Learning - Continual Learning. An Interview in Nature Outlook.
  • Continual Learning, and Continuous Learning: Learn like humans - accumulating the prevously learned knowledge and adapt/transfer it to help future learning.
  • New Survey: Continual Learning of Natural Language Processing Tasks: A Survey. arXiv:2211.12701, 11/23/2022.
  • Continual Pre-training of Language Models and Cruise - Cross-system Architecture Design for Automous Wireless Networks based on Lifelong Machine Learning.
• Open World Machine Learning to achieve AI Learning Autonomy.
  • Closed world assumption: classic machine learning assumes that test instances must be from training classes. Not true in many real-world environments.
  • Open-world learning: It involves novelty (out-of-distribution) dection, characterization, adaptation and continual learning of novelty/new classes.
• Autonomous Learning Agents or On-the-Job Continual Learning After Model Deployment for Autonomous and Interactive AI Systems.
  • AI Autonomy: AI agents learn on their own continuously after deployment. Learning is self-motivated and self-initiated.
  • Interactive AI. The key to autonomous learning is to learn by the agent itself through self-initiated interactions with humans, other agents and the environment.
  • NSF AI Institute: ACTION - Agent-based Cyber Threat Intelligence and Operation.
• Lifelong and Continual Learning Dialogue Systems
  • SIGIR-2022 and IJCAI-2021 Tutorial: Continual Learning Dialogue System - Learning after Model Deployment.
  • New Book: "Lifelong and Continual Learning Dialogue Systems." by Sahisnu Mazumder and Bing Liu, Feb 2024.
  
  
• Earlier projects (inactive currently)
  • PU Learning - Learning from Positive and Unlabeled Examples (Set Expansion is also a form of PU learning)
  • Detect Fake Reviews and Fake Reviewers - Detect Opinion Spam
    • Detecting fake or deceptive opinions on review sites, facebook, Twitter, Weibo, and forum discussion sites
  • Intent Mining - Mining user Intents or Intentions in Social Media Posts
    • Chapter 11 of my sentiment analysis book discusses this topic; also see this NAACL-HLT-2013 paper.
  • Generate a Book Automatically using Web Content: Synthesize knowledge on the Web (See our WWW-2003 paper).
    • Given a topic, discover sub-topics and related concepts, and synthesize information from multiple Web pages to present a coherent and fairly complete picture of the topic.
  • Web Data Extraction from Flat/Nested Data Records - Web page template detection and noise removal
    • Download MDR; Two new systems are DEPTA and NET.
  • Data Mining and Knowledge Discovery
    • Diagnostic Data Mining - Interestingness of Knowledge: Finding interesting and actionable knowledge.
      • We built a system in 2006, called Opportunity Map (also known as OM in Motorola). It was deployed in 2006 and is still in regular use in Motorola. Motorola has sold copies of the system to some Telco companies. There is also a patent, but unfortunately it is owned by Motorola, not us :-(.
    • Classification Based on Associations - Integrating Classification and Association Rule Mining
      • Download CBA - asociation rules based classification. You can also use the CBA implementation of Frans Coenen from Univesity of Livepool.
      • KDD-98 paper and others (KDD-99, PKDD-00, etc)
    • Association rule mining with multiple minimum supports
    • Interestingness: subjective interestingness, rule summary, rule query language, change mining

Keynote and Invited Talks (not updated) -- (recent talks on continual learning, some older talks)

1. Invited Talk. “Sentiment Analysis with Lifelong Learning.” ETS, December 7, 2015.
2. Invited Talk. “Sentiment Analysis with Lifelong Learning.” Brigham Young University, December. 3, 2015.
3. Keynote speech. “Sentiment Analysis, Lifelong Learning and Intelligent Personal Assistants.” The 2015 Conf. on Technologies and Applications of Artificial Intelligence (TAAI-2015). Taiwan, Nov. 20-22, 2015.
4. Invited talk. “Sentiment analysis and lifelong machine learning.” Frontiers in Computational Mathematics: AMS Central Fall Sectional Meeting, October 2-4, 2015.
5. Keynote speech. “The State of Sentiment.” Sentiment Analysis Symposium, New York City, July 15-16, 2015.
6. Invited tutorial. "Sentiment analysis: mining opinions, sentiments, and emotions." Sentiment Analysis Symposium, New York City, July 15-16, 2015.
7. Keynote speech. “Deception Detection via Pattern Mining of Web Usage Behavior” Workshop on Data mining for Big Data: Applications, Challenges & Perspectives, Morocco, 03/25/2015
8. Keynote speech. “Social Media Analysis via Continuous Learning.” Adobe Text Analytics Summit, Feb 26, 2015.
9. Some older talks

Teaching

• Fall 2024: CS 583 - Data Mining and Text Mining
• Spring 2024: CS 583 - Data Mining and Text Mining
• Fall 2023: CS 583 - Data Mining and Text Mining
• Spring 2023: CS 583 - Data Mining and Text Mining
• Fall 2022: CS 583 - Data Mining and Text Mining
• Summer 2022: Lifelong and Continual Learning. A Short PhD Course (8 hours), Aalborg University, June 14 and 16, 2022. (Bing Liu and Zixuan Ke)
• Spring 2022: CS 411 - Artificial Intelligence I
• Past teaching

Professional Recognitions and Memberships

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

fake reviews: 2 and 3