His primary scientific interests are in World Wide Web, Social media, Artificial intelligence, The Internet and Collaborative filtering. Daniel Zeng has included themes like Social influence and Interpersonal relationship in his Social media study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Transaction data and Machine learning.
His work on Web server is typically connected to CPU cache as part of general The Internet study, connecting several disciplines of science. His work carried out in the field of Collaborative filtering brings together such families of science as Hopfield network, Collaborative software, Random graph theory and Search algorithm. His Sentiment analysis study incorporates themes from Text mining, Information retrieval and Data science.
His primary areas of investigation include Data science, Artificial intelligence, Social media, Information retrieval and Data mining. His studies deal with areas such as Infectious disease, World Wide Web, Social computing and Data visualization as well as Data science. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Natural language processing.
His Social media research is multidisciplinary, relying on both Sentiment analysis, Cognitive psychology, Popularity and Empirical research. His Information retrieval research includes elements of Web page and The Internet. His Collaborative filtering study is concerned with the larger field of Recommender system.
His main research concerns Social media, Artificial intelligence, Data science, Deep learning and Advertising. Daniel Zeng has researched Social media in several fields, including Sentiment analysis, Popularity, Cognitive psychology and Empirical research. His Cognitive psychology study combines topics in areas such as Social computing, Sadness, Anger, Disgust and Centrality.
His research integrates issues of Machine learning and Natural language processing in his study of Artificial intelligence. The concepts of his Data science study are interwoven with issues in Identification and Complex network. His studies in Advertising integrate themes in fields like Incentive and Search engine advertising.
Social media, Data science, Popularity, Advertising and Social media analytics are his primary areas of study. His Social media research incorporates elements of Sentiment analysis and Public health surveillance. His Data science research incorporates themes from Network representation learning, Health informatics and Market research.
His Popularity research focuses on Purchasing and how it relates to User-generated content, Free riding, Word of mouth and Natural experiment. The Advertising study combines topics in areas such as Incentive and Search engine. Daniel Zeng combines subjects such as Social computing, Sadness, Cognitive psychology, Centrality and Disgust with his study of Social media analytics.
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Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
Zan Huang;Hsinchun Chen;Daniel Zeng.
ACM Transactions on Information Systems (2004)
Social Computing: From Social Informatics to Social Intelligence
Fei-Yue Wang;Daniel Zeng;K.M. Carley;W. Mao.
Social Media Analytics and Intelligence
Daniel Zeng;Hsinchun Chen;R Lusch;Shu-Hsing Li.
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
Zan Huang;D. Zeng;Hsinchun Chen.
IEEE Intelligent Systems (2007)
COPLINK: managing law enforcement data and knowledge
Hsinchun Chen;Daniel Zeng;Homa Atabakhsh;Wojciech Wyzga.
Communications of The ACM (2003)
Sentiment analysis of Chinese documents: From sentence to document level
Changli Zhang;Daniel Zeng;Jiexun Li;Fei-Yue Wang.
Journal of the Association for Information Science and Technology (2009)
CI Spider: a tool for competitive intelligence on the web
Hsinchun Chen;Michael Chau;Daniel Zeng.
decision support systems (2002)
A comparison of collaborative-filtering algorithms for ecommerce
Zan Huang;Daniel Zeng;Hsinchun Chen.
IEEE Intelligent Systems (2007)
Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems
Zan Huang;Daniel D. Zeng;Hsinchun Chen.
Management Science (2007)
Twitter Sentiment Analysis: A Bootstrap Ensemble Framework
Ammar Hassan;Ahmed Abbasi;Daniel Zeng.
international conference on social computing (2013)
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