Hsinchun Chen

Photo of Hsinchun Chen

Hsinchun Chen

Professor, Management Information Systems
Regents Professor
Member of the Graduate Faculty
Professor, BIO5 Institute
Primary Department
Management Information Systems
Department Affiliations
Management Information Systems
BIO5 Institute
Contact
(520) 621-4153
hchen@eller.arizona.edu

Research Interest

Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.

Publications

Chen, H., Atabakhsh, H., Wang, A. G., Kaza, S., Tseng, L. C., Wang, Y., Joshi, S., Petersen, T., & Violette, C. (2006). COPLINK center: Social network analysis and identity deception detection for law enforcement and homeland security intelligence and security informatics: A crime data mining approach to developing border safe research. ACM International Conference Proceeding Series, 151, 49-50.

Abstract:

In this paper, we describe the highlights of the COPLINK Center for law enforcement and homeland security project. Two new components of the project are described, namely, identity resolution and mutual information.

Chen, H. (2010). Business and market intelligence 2.0. IEEE Intelligent Systems, 25(1), 68-71.

Abstract:

Some articles on Business and Market Intelligence 2.0 from distinguished experts in marketing science, finance, accounting, and computer science, are presented. 'The Phase Transition of Markets and Organizations: The New Intelligence and Entrepreneurial Frontier', characterizes phase transition in markets and organizations as a move from individuals and resources being separate to being together. 'User- Generated Content on Social Media: Predicting New Product Market Success from Online Word of Mouth', explores the predictive validity of various text and sentiment measures of online word of mouth (WOM) for the market success of new products. 'On Data- Driven Analysis of User-Generated Content', discusses data-driven approaches, including content and network analysis that can be used to derive insights and characterize user-generated content from companies and other organizations.

Li, W., Yin, J., & Chen, H. (2016). Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications. IEEE Transactions on Knowledge and Data Engineering.
Chen, H., & Dhar, V. (1991). Cognitive process as a basis for intelligent retrieval systems design. Information Processing and Management, 27(5), 405-432.

Abstract:

Two studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of five computational models of online document retrieval. These models were then incorporated into the design of an "intelligent" document-based retrieval system. Following a discussion of this system, we discuss the broader implications of our research for the design of information retrieval systems. © 1991.

Benjamin, V., Chung, W., Abbasi, A., Chuang, J., Larson, C. A., & Chen, H. (2013). Evaluating text visualization: An experiment in authorship analysis. IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics, 16-20.

Abstract:

Analyzing authorship of online texts is an important analysis task in security-related areas such as cybercrime investigation and counter-terrorism, and in any field of endeavor in which authorship may be uncertain or obfuscated. This paper presents an automated approach for authorship analysis using machine learning methods, a robust stylometric feature set, and a series of visualizations designed to facilitate analysis at the feature, author, and message levels. A testbed consisting of 506,554 forum messages, in English and Arabic, from 14,901 authors was first constructed. A prototype portal system was then developed to support feasibility analysis of the approach. A preliminary evaluation to assess the efficacy of the text visualizations was conducted. The evaluation showed that task performance with the visualization functions was more accurate and more efficient than task performance without the visualizations. © 2013 IEEE.

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