PA-DHK: Polarity analysis for discovering hidden knowledge

J. D. Kim, J. Son, H. Peter In, S. H. Hwang, H. Lee, D. K. Baik

Research output: Contribution to journalArticlepeer-review

Abstract

In a Social Network Service (SNS), a large amount of data with a variety of characteristics is generated through voluntary participation of users. These data are called "Big Social Data." Big social data can identify not only content registered on the web but also the relations of the friends of users. One of the most representative studies on SNS is analysis of the characteristics of social content and social relations, because SNS users tend to add people who are in close contact with them and have similar interests to their list of friends. Finding new knowledge from these large amounts of big social data can be very useful. This paper proposes a polarity analysis method for discovering hidden knowledge based on formal concept analysis in SNSs called PA-DHK. Further, we show, via experiments, that our data analysis approach can be applied to knowledge discovery using association rules.

Original languageEnglish
Pages (from-to)2198-2208
Number of pages11
JournalScientia Iranica
Volume22
Issue number6
Publication statusPublished - 2015

Bibliographical note

Funding Information:
This research was supported by a Korea University Grant, and the Basic Science Research Program, through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02036442), and the Next-Generation Information Computing Development Program through the NRF funded by the Ministry of Science, ICT & Future Planning (2012M3C4A7033346). The authors thank Daumsoft, for assistance with experiment data (Twitter data crawling).

Publisher Copyright:
© 2015 Sharif University of Technology. All rights reserved.

Keywords

  • Formal concept analysis
  • Knowledge discovery
  • Polarity analysis
  • Social network services
  • Twitter content

ASJC Scopus subject areas

  • General Engineering

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