• Journal of Digital Contents Society
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• v.15 no.5
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• pp.621-630
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• 2014

The purpose of this study is to extract metaphor characteristics of Social Network Service. Social Network Service is different from existing media only available in one-way communication, each individual expands opinion by sharing daily life and opinion directly and interpreting another user's post. This Study premise that the reason of converting from passive reader to active enunciator is the metaphor characteristics of Social Network Service by 'Source Domain' and 'Target Domain'. In addition, this study examines the meaning of structure user's text production and interpreting based triple mimesis of Paul Ricoeur. This study has significance as arguing with existing study on SNS as metonymic media and suggesting metaphor characteristics and meaning of Social Network Service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4701-4719
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• 2015

A Mobile Ad hoc Network (MANET) consists of mobile nodes which co-operate to forward each other's packets without the presence of any centralized authority. Due to this lack of centralized monitoring authority, MANETs have become vulnerable to various kinds of routing misbehaviour. Sometimes, nodes exhibit non-cooperating behaviour for conserving their own resources and exploiting others' by relaying their traffic. A node may even drop packets of other nodes in the guise of forwarding them. This paper proposes an efficient Reputation and Collaboration technique through route-request for handling such misbehaving nodes. It lays emphasis not only on direct observation but also considers the opinion of other nodes about misbehaving nodes in the network. Unlike existing schemes which generate separate messages for spreading second-hand information in the network, nodes purvey their opinion through route-request packet. Simulation studies reveal that the proposed scheme significantly improves the network performance by efficiently handling the misbehaving nodes in the network.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.9
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• pp.59-70
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• 2016

The rapid development of the Chinese version of Twitter, the groom Weibo has become an important communication means for Chinese SNS users to obtain and share information. As a result, in China, the phenomenon of the power shift has emerged from the traditional opinion leaders to SNS opinion leasers. The relationship analysis of demographic variables of the Chinese SNS users and their Information on the relationship between keywords was made by utilizing the centrality analysis using Social Network Program NetMiner. China's SNS opinion leaders have general interest in daily activities with their families or friends rather than in social issues. And in case of SNS opinion leaders of high betweenness centrality, it was analyzed that general users was a key mediator role that organically out lead to the adjacent information. These properties are not independent of demographic variables, such as professional, therefore, the demographic characteristics of SNS opinion leaders showed a significant effect on the parameters of betweenness centrality. This study analyzed the characteristics of SNS users, especially opinion leaders in China by looking at the aspects of Chinese social phenomenon in terms of information. Through this study, we expect to provide basic information about the social characteristics of China through collective communication.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.201-209
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• 2013

This study is an application of facebook (FB) as a strategic communication of viral marketing. Old fashioned advertising and public relation in facebook are not effective at social network service(SNS) environment, and a different marketing communication is needed because facebook is essentially oriented for building relationship among people. The study assumes that an opinion leader might exist at facebook and tries to find out how facebook users show a pattern of communication based on level of opinion leadership regarding a product. The findings showed that people with high opinion leadership communicated more actively than people with low opinion leadership. This study will contribute to segment seed consumers using opinion leadership in facebook.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.201-210
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• 2024

In empirical software engineering research, there is an increased use of questionnaires and surveys to collect information from practitioners. Typically, such data is then analyzed based on overall, descriptive statistics. Overall, they consider the whole survey population as a single group with some sampling techniques to extract varieties. In some cases, the population is also partitioned into sub-groups based on some background information. However, this does not reveal opinion diversity properly as similar opinions can exist in different segments of the population, whereas people within the same group might have different opinions. Even though existing approach can capture the general trends there is a risk that the opinions of different sub-groups are lost. The problem becomes more complex in case of longitudinal studies where minority opinions might fade or resolute over time. Survey based longitudinal data may have some potential patterns which can be extracted through a clustering process. It may reveal new information and attract attention to alternative perspectives. We suggest using a data mining approach to finding the diversity among the different groups in longitudinal studies (agile skeptics). In our study, we show that diversity can be revealed and tracked over time with the use of clustering approach, and the minorities have an opportunity to be heard.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1109-1116
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• 2010

In this paper, some performance characteristics of VoIP(Voice over Internet Protocol) for MANET(Mobile Ad-hoc Networks) with simulation is studied and appropriate condition for implementation of VoIP service is suggested. VoIP simulator is implemented with NS(Network Simulator)-2. VoIP traffic for simulation is generated with some codecs of G.711, G.723.1, G.726-32, G.729A, GSM.AMR and iLBC. As simulation results for traffic transmission under $670{\times}670m$ 50node MANET environment, performance data for MOS(Mean Opinion Score), network delay, packet loss rate and transmission bandwidth are measured. Normalized analysis about measured results shows that maximum VoIP connection satisfying VoIP service quality condition is 15.

• Journal of Korean Clinical Nursing Research
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• v.25 no.3
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• pp.209-220
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• 2019

Purpose: A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis. Methods: From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as "nurse", "Taeum", and "bullying" were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence. Results: This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, "nurse", "hospital", "patient", and "physician" were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters. Conclusion: This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.7
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• pp.767-772
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• 2014

As temporary network, ad-hoc network has been effected by structures and implemented environments of networks. In this paper, transmission performance of lattice structure ad-hoc network, which is expected to use in sensor network and IoT(Internet of Things), is analyzed in point of intrusions and countermeasure for intrusion is suggested. In this paper, computer simulation based on NS-2 is used for performance analysis, VoIP(Voice over Internet Protocol) as a widely used service is chosen for performance measure. MOS(Mean Opinion Score) and call connection rate is used as performance parameter. As results of performance analysis, it is shown that for MOS, random network is better then lattice network at intrusion environments, but for call connection rate, lattice network is better then random network.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.9-15
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• 2018

Policy polls, which investigate the degree of support that the policy has for policy implementation, play an important role in making decisions. As the number of Internet users increases, the public is actively commenting on their policy news stories. Current policy polls tend to rely heavily on phone and offline surveys. Collecting and analyzing policy articles is useful in policy surveys. In this study, we propose a method of analyzing comments using deep learning technology showing outstanding performance in various fields. In particular, we designed various models based on the recurrent neural network (RNN) which is suitable for sequential data and compared the performance with the support vector machine (SVM), which is a traditional machine learning model. For all test sets, the SVM model show an accuracy of 0.73 and the RNN model have an accuracy of 0.83.