• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.89-96
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• 2018

In online communities, a large number of participants can exchange their opinion using replies without time and space restrictions. While the online space provides quick and free communication, it also easily triggers unnecessary quarrels and conflicts. The network established on the discussion participants is an important cue to analyze the confrontation and predict serious disputes. In this paper, we present a quantitative measure for polarity observed on the discussion network built from reply exchanges in online communities. The proposed method uses the comment exchange information to establish the user interaction network graph, computes its maximum spanning tree, and then performs vertex coloring to assign two colors to each node in order to divide the discussion participants into two subsets. Using the proportion of the comment exchanges across the partitioned user subsets, we compute the polarity measure, and quantify how discussion participants are bipolarized. Using experimental results, we demonstrate the effectiveness of our method for detecting polarization and show participants of a specific discussion subject tend to be divided into two camps when they debate.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.6
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• pp.427-433
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• 2007

Generally, H.V.A.C load capacity in early planning phase can presume with maximum thermal load. Basic data can prove by air conditioning equipment system data analysis at existing building. There are poor and not reliable alternative presentation. In this paper, measured data after use H.V.A.C load calculation K-load program reply choosing standard building and variables simulation. And I founded peak load correlation graph and mode for several kinds of variable and contents of size. I wish that equipment designer is beaconed to produce optimum capacity at building as quantitative through this result.

• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.41-51
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• 2006

Educators have recognized the usefulness of the e-BBS(electronic-Bulletin Board System) as an asynchronous communication medium for collaborative learning. However, theoretical outcomes to understand the structural analysis of thc e-BBS are not adequate enough. As a consequence, we are left with inadequate supportive tools to understand complex communication phenomena and to assist teachers to guide students to educational purposes. This can cause teachers to have a laissez-faire approach using BBS as a communication medium for education due to the lack of understanding the structural and overall situation. To address this issue, this paper presents a theoretical foundation to help understand the structure of discussion activities via the e-BBS and then presents a computational model, based on the structural analysis, for the behavioral analysis of participants. The formal representation of bi-directional discussion activities is realized by devising two graphs - the reply graph and the connection graph. Various measurements and statistics for the analysis are presented in the paper. To clarify the analysis, we classify the results produced through the analysis of discussion activities and participants' behaviors.