• Journal of IKEEE
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• v.20 no.3
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• pp.235-240
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• 2016

In this study, we use the data collected from Twitter, as an SNS(Social Networking Service), for service innovation. This data was collected and processed by Flume. The data set in May 2016 was 4,766 and 15,543 from company S and company X, respectively. We were able to figure out the emotional atmosphere of the two companies through the sentiment analysis(SA) and to find out about the vertical relationship through the bibliometric analysis(BA). Furthermore, we were able to grasp the horizontal relationship through the social network analysis(SNA). It was concluded that SNS was worth while to derive an innovative item.

• Korean Journal of Social Welfare Studies
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• v.45 no.2
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• pp.375-402
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• 2014

The purpose of this study is to investigate the longitudinal change pattern of life satisfaction and the effect of social capital of people with disabilities. For this study, data were drawn from the panel data employment for the disabled from the second to the fifth. It includes 3,206 person with disabilities. The methodology adopted in this study is the latent growth curve modeling. The findings of this study are as follows: First, Life satisfaction has been decreased over years and the difference between individuals of life satisfaction of people with disabilities were statistically significant. Second, The intercept of life satisfaction in the disabled was positively associated with network, and slope of life satisfaction was negatively related to network. Third, social participation positively influences the intercept of life satisfaction but, no significant affect to the slope. Based on results of the study, practical implication and the political-approach to interventions for the better life satisfaction of the disabled are being discussed.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.1-21
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• 2018

This study aims to investigate the usage patterns of users in Social Network Services (SNS) where is an upsurge. Specifically, the paper considers the reason why young people more and more prefer online (or mobile) SNS activities rather than offline face-to-face social relationship. Furthermore, the drivers which affect SNS usages are considered from users' personal characteristics and social influences. User's personal characteristics include their personalities (extraversion and introversion), narcissism, and life satisfaction. Social influences involve subjective norm, visibility, and image. Affected by personal and social factors in SNS, users intend to show positive self-presentation, which refers to a behavior to selectively expose his/her goodness to others. As one of the most influential drivers affecting SNS usage, the positive self-representation has an effect on the level of SNS usage. Thus, this paper suggests the hypothesized research model focusing on positive self-representation in the relationship among personal characteristics, social influence, and user's behavior in SNS. Empirical data analysis with 100 questionnaires suggests that all hypotheses were adopted except for the effect of visibility among social influence factors on positive self-representation.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.426-439
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• 2021

To alleviate the cold-start problem and data sparsity in web service recommendation and meet the personalized needs of users, this paper proposes a personalized web service recommendation method based on a hybrid social network and multi-objective immune optimization. The network adds the element of the service provider, which can provide more real information and help alleviate the cold-start problem. Then, according to the proposed service recommendation framework, multi-objective immune optimization is used to fuse multiple attributes and provide personalized web services for users without adjusting any weight coefficients. Experiments were conducted on real data sets, and the results show that the proposed method has high accuracy and a low recall rate, which is helpful to improving personalized recommendation.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.616-631
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• 2019

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.

• The Journal of Information Systems
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• v.32 no.3
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• pp.41-61
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• 2023

Purpose This study aims to categorize the types of health, analyze the effects among health types based on network analysis find the most important type of health, and explain whether the results between health types vary depending on demographic characteristics. Design/methodology/approach This study investigated individual physical, clinical, mental, and social health(social capital and social support) levels through a survey of 100 people. Network analysis was applied to the survey data to confirm the degree centrality of nodes. Furthemore, we investigated the differences in core nodes according to gender and age groups. Findings According to the analysis result, social support was the most important health type in the entire group. Furthermore, the importance of health type was different depending on the characteristics of the groups. In the case of men, clinical health was the most important health type, and social support was analyzed to be the most important for women. In the case of young people, clinical health was the most important health type, and mental health was the most important health type in the middle-aged.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.57-66
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• 2017

Lately, academic interests in big data analysis and social network has been prominently raised. Various academic fields are involved in this social network based research trend, which is, social network has been actively used as the research topic in social science field as well as in natural science field. Accordingly, this paper focuses on the text analysis and the following social network analysis with the Master's and Doctor's dissertations. The result indicates that certain words had a high frequency throughout the entire period and some words had fluctuating frequencies in different period. In detail, the words with a high frequency had a higher betweenness centrality and each period seems to have a distinctive research flow. Therefore, it was found that the subjects of the Master's and Doctor's dissertations were changed sensitively to the development of IT technology and changes in information curriculum of elementary, middle and high school. It is predicted that researches related to smart, mobile, smartphone, SNS, application, storytelling, multicultural, and STEAM, which had an increased frequency in period 4, would be continuously conducted. Moreover, the topics of robots, programming, coding, algorithms, creativity, interaction, and privacy will also be studied steadily.