Performance and Analysis Public Options Based on Using K-Means Algorithm

Authors(2) :-T. G. Babu, R. Sharmila

The hot spot in the general public are frequently the most ready to be found, shared and remarked by we-media like Facebook or Twitter. Mining hot spot from we-media can assist people to optimize their own investment behavior, assist enterprises to adjust their production and investment strategies to meet market demand, and help government to monitor public opinions and grab the chance to direct the healthy development of public opinions. In this paper, where the authors confronted a need to propose an exact calculation for news bunching that might assemble news into semantically close sets. A two phase way to deal that objective is proposed. First comparability estimation between news messages is performed using semantic similarity metric in view of WordNet. Next, the most reasonable for given data structure bunching calculations is chosen so as to acquire topical news groups and watch their size circulation after some time. Experiments were made on news volumes from several news mass media official pages in Facebook.

Authors and Affiliations

T. G. Babu
Assistant Professor, PG &Research Department of Computer Science and Science and Applications, Arignar Anna Govt Arts College Arcot Road, Cheyyar, Vellore, Tamil Nadu, India
R. Sharmila
M.Phil (CS) Research Scholar PG &Research Department of Computer Science and Science and Applications, Arignar Anna Govt Arts College Arcot Road, Cheyyar, Vellore, Tamil Nadu, India

We-media, Hot spot clustering, K-Means, psychological warfare, ontology, similarity analysis, news clustering, and news analysis.

  1. Brandyn White, "Web-Scale Computer Vision using Map Reduce for Multimedia Data Mining", MDMKDD '10 Proceedings of the Tenth International Workshop on Multimedia Data Mining. ACM New York, pp.287-292, 2010.
  2. Zhang Yu-feng, "Identifying Opinion Sentences and Opinion Holders in Internet Public Opinion", Industrial Control and Electronics Engineering (ICICEE), pp.1668- 1671, 2012.
  3. Sun Shengping, "Detection and tracking technology research for Chinese microblogging hot topic", Beijing Jiaotong University School of Economics and Management, pp.18-48, 2011.
  4. Liu Xiaodong, "Construction of topic detection and tracking system", Computer Science, Beijing University of Posts and Telecommunications, pp.9-50, 2011.
  5. S. Phuvipadawat, T. Murata, "Breaking news detection and tracking in Twitter", 2010IEEE/WIC/ACM International Conference on Web Intelligent Agent Technology, pp.120-123, 2010
  6. H. Liu and J. H. Xu, "Research of internet public opinion hotspot detection," Bulletin of Science and Technology, vol. 27, no. 3, pp. 421–425, 2011.
  7. G. Hamerly and C. Elkan, "A new algorithm based on K-means and its application in internet public opinion hotspot detection," Pattern Recognition, vol. 32, no. 6, pp. 521–534, 2012.
  8. L. M. Kristina, "Document clustering in reduced dimension vector space," Journal of Computer Application, vol. 27, no. 10, pp. 37–49, 2011.
  9. H. J. Andreas, "Research on text document clustering," Com-puter Simulation, vol. 24, no. 7, pp. 84–99, 2010.
  10. C. D. Wagstaf f and S. S. Rogers, "Constrained K-means clustering with background knowledge," Journal of Computer Engineering and Application, vol. 21, no. 5, pp. 467–479, 2011.
  11. B. T. Ya, "Research on public opinion hotspot detection based on SVM," Science and Technology Management Research, vol. 25, no. 2, pp. 64–69, 2009.
  12. P. S. Bradley and L. S. Managasarian, "K-plane clustering," Jour-nal of Global Optimization, vol. 16, pp. 23–32, 2010.
  13. Y. Tang and Q. S. Rong, "An implementation of clustering alg-orithm based on K-means," Journal of Hubei Institute For Nationalities, vol. 22, no. 1, pp. 69–71, 2011.
  14. Z. H. Yang and Y. T. Yang, "Document clustering method based on hybrid of SOM and K-means," Computer Application, vol. 27, no. 5, pp. 73–75, 2012.
  15. Y. F. Zhang and J. L. Mao, "An improved K-means algorithm," Computer Application, vol. 23, no. 8, pp. 31–33, 2009.
  16. N. Li and D. D. Wu, "Using text mining and sentiment analysis for online forums hotspot detection and forecast," Decision Support Systems, vol. 48, no. 2, pp. 354–368, 2010.
  17. T. Pedersen "Information Content Measures of Semantic Similarity Perform Better Without Sense-Tagged Text". Proceeding HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, p. 329-332.
  18. Bach, F., Jordan, M.: Learning spectral clustering. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems 16 (NIPS), pp. 305–312. MIT Press, Cambridge, 2004.

Publication Details

Published in : Volume 4 | Issue 9 | July-August 2018
Date of Publication : 2018-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 603-614
Manuscript Number : IJSRSET1849133
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

T. G. Babu, R. Sharmila, " Performance and Analysis Public Options Based on Using K-Means Algorithm, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.603-614, July-August-2018.
Journal URL :

Article Preview