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

Authors

  • 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

Keywords:

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

Abstract

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.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-08-30

Issue

Volume 4, Issue 9, July-August-2018

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
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.