Filtering of Unwanted Messages from OSN User Walls
International Journal of Emerging Trends in Science and Technology,
Vol. 2 No. 01 (2015),
6 January 2015
Abstract
An information filtering scheme is an information organization designed in support of unstructured or semi structured information. Filtering applications naturally entail streams concerning incoming information, moreover being transmitted by distant sources. Text categorization is accordingly a discipline at intersection of machine learning and information retrieval and as such it contributes to a numeral of characteristics by means of other responsibilities. Filtering was used to explain the procedure of accessing as well as retrieving information from distant databases, in which incoming information is the consequence of database searches. To enforce the filtering rules precise by the user the initial component makes use of the categorization of message provided by means of the module of short text classifier.
Keywords: Filtering rules, Short text classifier, Database, Machine learning.How to Cite
Download Citation
References
2. M. Chau and H. Chen, “A machine learning approach to web page filtering using content and structure analysis,†Decision Support Systems, vol. 44, no. 2, pp. 482–494, 2008.
3. R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,†in Proceedings of the Fifth ACM Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195–204.
4. F. Sebastiani, “Machine learning in automated text categorization,†ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, 2002.
5. M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-based filtering in on-line social networks,†in Proceedings of ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010), 2010.
6. N. J. Belkin and W. B. Croft, “Information filtering and information retrieval: Two sides of the same coin?†Communications of the ACM, vol. 35, no. 12, pp. 29–38, 1992.
7. P. J. Denning, “Electronic junk,†Communications of the ACM, vol. 25, no. 3, pp. 163–165, 1982.
8. P. W. Foltz and S. T. Dumais, “Personalized information delivery: An analysis of information filtering methods,†Communications of the ACM, vol. 35, no. 12, pp. 51–60, 1992.
9. P. S. Jacobs and L. F. Rau, “Scisor: Extracting information from online news,†Communications of the ACM, vol. 33, no. 11, pp. 88–97,1990.
10. S. Pollock, “A rule-based message filtering system,†ACM Transactions on Office Information Systems, vol. 6, no. 3, pp. 232–254, 1988.
11. P. E. Baclace, “Competitive agents for information ï¬ltering,†Communications of the ACM, vol. 35, no. 12, p. 50, 1992.
12. P. J. Hayes, P. M. Andersen, I. B. Nirenburg, and L. M. Schmandt “Tcs: a shell for content-based text categorization,†in Proceedings 6th IEEE Conference on Artiï¬cial Intelligence Applications (CAIA90). IEEE Computer Society Press, Los Alamitos, US, 1990, pp320–326.
13. G. Amati and F. Crestani, “Probabilistic learning for selective dissemination of information,†Information Processing and Management, vol. 35, no. 5, pp. 633–654, 1999.
M. J. Pazzani and D. Billsus, “Learning and revising user proï¬les: The identiï¬cation of interesting web sites,†Machine Learning, vol. 27, no. 3, pp. 313–331, 1997
- Article Viewed: 71 Total Download
