Earticle

초록

영어

Web data mining is a hot research topic that is a technique used to crawl through various web resources to collect required information. As the importance of such web data mining has been recognized, extensive studies have been conducted actively to analyze the data in a Social Networking Service (SNS). In a SNS, a large amount of data, which has a variety of characteristics, is generated through voluntary participation of users, which is also 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. In this paper, we introduce Formal Concept Analysis (FCA) as the basis for a practical and well founded methodological approach for web data analysis which identifies conceptual structures among data sets. As well as, we propose a framework for discovering hidden knowledge by using polarity from Twitter contents. Additionally, we show the experiments that demonstrate how our framework can be applied for knowledge discovery.

목차

Abstract
 1. Introduction
 2. Framework
  2.1. Polarity Analysis
  2.2. Formal Concept Analysis (FCA)
  2.3. Proposed Framework
 3. Experiment and Result
  3.1. Experiment
  3.2. Result
 4. Conclusions
 References

키워드

저자

• Jeong-Dong Kim [ Department of Computer Science and Engineering, Korea University, Anam-Dong Sungbuk-Gu, Seoul, Republic of Korea ]
• Suk-Hyung Hwang [ Department of Computer Science & Engineering, Sunmoon University, Kalson-Ri, Tangjeong- Myoon, Asan-Si, Chungnam, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605