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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 903765, 8 pages
http://dx.doi.org/10.1155/2013/903765
Research Article

Two Applications of Clustering Techniques to Twitter: Community Detection and Issue Extraction

1Department of Computer Science and Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 139-701, Republic of Korea
2Tmaxsoft, Bundang-gu, Seongnam-si, Gyeonggi-do 463-824, Republic of Korea
3SK Telecom, Jung-gu, Seoul 100-999, Republic of Korea
4Future IT R&D Laboratory, LG Electronics Umyeon R&D Campus, 38 Baumoe-ro, Seocho-gu, Seoul 137-724, Republic of Korea

Received 25 July 2013; Revised 25 October 2013; Accepted 31 October 2013

Academic Editor: Daniele Fournier-Prunaret

Copyright © 2013 Yong-Hyuk Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Twitter’s recent growth in the number of users has redefined its status from a simple social media service to a mass media. We deal with clustering techniques applied to Twitter network and Twitter trend analysis. When we divide and cluster Twitter network, we can find a group of users with similar inclination, called a “Community.” In this regard, we introduce the Louvain algorithm and advance a partitioned Louvain algorithm as its improved variant. In the result of the experiment based on actual Twitter data, the partitioned Louvain algorithm supplemented the performance decline and shortened the execution time. Also, we use clustering techniques for trend analysis. We use nonnegative matrix factorization (NMF), which is a convenient method to intuitively interpret and extract issues on various time scales. By cross-verifying the results using NFM, we found that it has clear correlation with the actual main issue.