User:Denmum/Bing Liu (computer scientist)

Bing Liu: What I plan to contribute
On the Bing Liu article, I plan to add more detailed information about Bing Liu's research as published in peer-reviewed academic journals. Currently, there are only a few sentences that do not go into much detail about what his research is actually about. I also intend on updating the list of articles to include all the peer-reviewed articles that Bing Liu has worked on.

Notes for Improvement/What is missing?

 * Adding peer-reviewed article list to Wikipedia page.
 * Posting more detailed information about Bing Liu's contributions on the topics he has specialized in such as sentiment analysis.

Possible Additional Sections

 * Peer-reviewed article list.
 * Detailed contributions on sentiment analysis/opinion mining.

Related Wikipedia Articles to look at

 * Sentiment analysis
 * Associative classifier
 * Machine learning
 * Natural language processing

More info on association rules for prediction
Association rule based classification/prediction 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, that one is trying to classify items into. 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. Liu and his collaborators described a new association rule based classification algorithm that takes into account the relationship between an item, two items, etc. and the positive and negative classes and gives each item a probability likelihood 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.

More info on sentiment analysis/opinion mining
In a paper that Liu collaborated on, the authors study the relationship between opinion lexicons (word sets) and opinion targets (or topics on which there is an opinion). They 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. Double propagation is the back and forth functional process between the word set and topic as the word set updates itself. Some algorithms require set rules and thus are limited in what they can actually do and in what service they provide in providing updated opinion lists. Their algorithm only requires an initial word set (or opinion lexicon), which is updated through finding relations between the words in the set and the target word or vice versa. The algorithm is done on a word population such as a set of sentences or a paragraph.

Articles(Peer-reviewed Article List)

 * Liu, Bing, Yiming Ma, Ching Kian Wong, and Philip S. Yu. 2003. “Scoring the Data Using Association Rules.” Applied Intelligence 18(2):119–35.
 * Qiu, Guang, Bing Liu, Jiajun Bu, and Chun Chen. 2011. “Opinion Word Expansion and Target Extraction through Double Propagation.” Computational Linguistics 37(1):9–27.


 * Wu, Xindong et al. 2007. “Top 10 Algorithms in Data Mining.” Knowledge and Information Systems 14(1):1–37.
 * Liu, Bing. 1995. “A Unified Framework for Consistency Check.” International Journal of Intelligent Systems 10(8):691–713.
 * Zhang, Lei, Shuai Wang, and Bing Liu. 2018. “Deep Learning for Sentiment Analysis: A Survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4).
 * Wang, Guan, Sihong Xie, Bing Liu, and Philip S. Yu. 2012. “Identify Online Store Review Spammers via Social Review Graph.” ACM Transactions on Intelligent Systems and Technology 3(4):1–21.
 * Yu, Zeng et al. 2019. “Reconstruction of Hidden Representation for Robust Feature Extraction.” ACM Transactions on Intelligent Systems and Technology 10(2):1–24.
 * Wang, Jing, Clement T. Yu, Philip S. Yu, Bing Liu, and Weiyi Meng. 2015. “Diversionary Comments under Blog Posts.” ACM Transactions on the Web 9(4):1–34.
 * Bing Liu, Wynne Hsu, Lai-Fun Mun, and Hing-Yan Lee. 1999. “Finding Interesting Patterns Using User Expectations.” IEEE Transactions on Knowledge and Data Engineering 11(6):817–32.
 * Yanhong Zhai and Bing Liu. 2006. “Structured Data Extraction from the Web Based on Partial Tree Alignment.” IEEE Transactions on Knowledge and Data Engineering 18(12):1614–28.
 * Yu, Huilin, Tieyun Qian, Yile Liang, and Bing Liu. 2020. “AGTR: Adversarial Generation of Target Review for Rating Prediction.” Data Science and Engineering 5(4):346–59.
 * Bing Liu. 1997. “Route Finding by Using Knowledge about the Road Network.” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 27(4):436–48.
 * Liu, Bing. 1993. “Problem Acquisition in Scheduling Domains.” Expert Systems with Applications 6(3):257–65.
 * Liu, Bing. 1993. “Knowledge-Based Factory Scheduling: Resource Allocation and Constraint Satisfaction.” Expert Systems with Applications 6(3):349–59.
 * Bing Liu, R. Grossman, and Yanhong Zhai. 2004. “Mining Web Pages for Data Records.” IEEE Intelligent Systems 19(06):49–55.
 * Bing Liu, Wynne Hsu, Shu Chen, and Yiming Ma. 2000. “Analyzing the Subjective Interestingness of Association Rules.” IEEE Intelligent Systems 15(5):47–55.
 * Liu, Bing and Alexander Tuzhilin. 2008. “Managing Large Collections of Data Mining Models.” Communications of the ACM 51(2):85–89.
 * Liu, Qian, Zhiqiang Gao, Bing Liu, and Yuanlin Zhang. 2016. “Automated Rule Selection for Opinion Target Extraction.” Knowledge-Based Systems 104:74–88.
 * Liu, Bing. 2017. “Lifelong Machine Learning: a Paradigm for Continuous Learning.” Frontiers of Computer Science 11(3):359–61.
 * Poria, Soujanya, Ong Yew Soon, Bing Liu, and Lidong Bing. 2020. “Affect Recognition for Multimodal Natural Language Processing.” Cognitive Computation 13(2):229–30.
 * Qian, Yuhua, Hang Xu, Jiye Liang, Bing Liu, and Jieting Wang. 2015. “Fusing Monotonic Decision Trees.” IEEE Transactions on Knowledge and Data Engineering 27(10):2717–28.
 * Wang, Hao, Yan Yang, Bing Liu, and Hamido Fujita. 2019. “A Study of Graph-Based System for Multi-View Clustering.” Knowledge-Based Systems 163:1009–19.
 * Li, Huayi, Bing Liu, Arjun Mukherjee, and Jidong Shao. 2014. “Spotting Fake Reviews Using Positive-Unlabeled Learning.” Computación y Sistemas 18(3).
 * Zhai, Zhongwu, Bing Liu, Jingyuan Wang, Hua Xu, and Peifa Jia. 2012. “Product Feature Grouping for Opinion Mining.” IEEE Intelligent Systems 27(4):37–44.
 * Apte, Chidanand, Bing Liu, Edwin P. Pednault, and Padhraic Smyth. 2002. “Business Applications of Data Mining.” Communications of the ACM 45(8):49–53.
 * Li, Yanni et al. 2020. “ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering.” IEEE Transactions on Knowledge and Data Engineering 1–1.
 * Liu, Bing. 2010. “Sentiment Analysis: A Multi-Faceted Problem.” IEEE Intelligent Systems.
 * Robert Grossman, Pavan Kasturi, Donald Hamelberg, and Bing Liu. 2004. "An Empirical Study of the Universal Chemical Key Algorithm for Assigning Unique Keys to Chemical Compounds." Journal of Bioinformatics and Computational Biology 02(01):155–71.
 * Liu, Bing et al. 1994. “Finding the Shortest Route Using Cases, Knowledge, and Djikstra's Algorithm.” IEEE Expert 9(5):7–11.
 * Liu, Bing. 1994. "Specific Constraint Handling in Constraint Satisfaction Problems.” International Journal on Artificial Intelligence Tools 03(01):79–96.