Analysis of the user behavior and opinion classification based on the BBS

Applied Mathematics and Computation

Volumn 205, Issue 2, 2008, Pages 668-676

  Huang, Weitong ^a   Zhao, Yu ^a   Yang, Shiqiang ^a   Lu, Yuchang ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

BBS; Data mining; Frequent set; Text classification; User behavior; User opinion

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; BEHAVIORAL RESEARCH; BOOLEAN FUNCTIONS; DATA MINING; DECISION MAKING; DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; MINES; MINING; NETWORK PROTOCOLS; SENSOR NETWORKS;

BBS; FREQUENT-SET; TEXT CLASSIFICATION; USER BEHAVIOR; USER OPINION;

TEXT PROCESSING;

EID: 54249088833     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2008.01.038     Document Type: Article

References (18)

1. Griffths M. Internet addiction: does it really exist?. In: Gackenbach J. (Ed). Psychology of the Internet (1998), Academic Press, New York
2. Kraut R., and Patterson M. Internet paradox - a social technology that reduces social involvement and psychological well-being?. American Psychologist 53 9 (1998) 1017-1031
3. U. Matzat, Social networks and cooperation in electronic communities: a theoretical-empirical analysis of academic communication and Internet discussion Groups: [Dissertation], University of Groningen, Amsterdam, 2001.
4. Hongxun Gong, The Real Expression of Virtual World - Take ytht.net of Peking University as an example (2001).
5. Wei Cui, Between Reality and Virtual World - Researches on ytht.net (2001).
6. Rafaeli S., and Project H. A collaborative quantitative study of computer-mediated communication. In: Sudweeks F., McLaughlin M., and Rafaeli S. (Eds). Network & Netplay: Virtural Groups on the Internet (1998), AAAI Press/MIT Press, Menlo Park 265-281
7. Berthold M. It makes sense: using an autoassociative neural network to explore typicality in computer mediated discussions. In: Sudweeks F., McLaughlin M., and Rafaeli S. (Eds). Network & Netplay: Virtural Groups on the Internet (1998), AAAI Press/MIT Press, Menlo Park
8. I. Langner, An introduction to Internet mailinglist research (2001). .
9. U. Matzat, Social networks and cooperation in electronic communities: a theoretical-empirical analysis of academic communication and Internet discussion Groups: [Dissertation], University of Groningen, Amsterdam, 2001.
10. D. Lewis, Naive (bayes) at forty: the independence assumption in information retrieval, in: 10th European Conference on Machine Learning (ECML-98), 1998, pp. 4-15.
11. I. Moulinier, Is learning bias an issue on the text categorization problem? in: Technical Report, LAFORIA-LIP6, University Paris VI, 1997.
12. Moulinier I., and Ganascia J.-G. Applying an existing machine learning algorithm to text categorization. In: Wermter S., Riloff E., and Scheler G. (Eds). Connectionist statistical, and symbolic approaches to learning for natural language processing. Lecture Notes for Computer Science series, number 1040 (1996), Springer-Verlag, Germany
13. Dasarathy B.V. Nearest Neighbor Norms: NN Pattern Classification Techniques. Los Alamitos (1991), IEEE Computer Society Press, CA
14. Yang Y. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval (1999)
15. Han J., and Kamber M. Data mining: Concepts and Techniques (2001), Academic Press, Morgan Kaufmann Publishers
16. R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, in: Proceedings of VLDB 94, Santiago de Chile, Chile, 1994, pp. 487-499.
17. O.R. Zaiane, M. Antonie, Classifying text documents by associating terms with text categories, in: Proceedings of the 13th Australasian Database Conference (ADC), 2002.
18. Zhao Y., Huang W.T., and Lu Y.C. Improvement on ARC-BC algorithm in text classification method. Lecture Notes in Control and Information Sciences 344 (2006) 903-908