• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.37-48
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• 2022

With development of Digital Technology, social issues are communicated through digital-based platform such as SNS and form public opinion. This study attempted to analyze big data from Twitter, a world-renowned social network service, and find out the public opinion. After collecting Twitter data based on 14 keywords for 1 year in 2021, analyzed the term-frequency and relationship among keyword documents with pearson correlation coefficient using Data-mining Technology. Furthermore, the 6 main topics that on the center of information security field in 2021 were derived through topic modeling using the LDA(Latent Dirichlet Allocation) technique. These results are expected to be used as basic data especially finding key agenda when establishing strategies for the next step related industries or establishing government policies.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.99-106
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• 2020

The purpose of this study is to find out whether image similarity between celebrities and brands on social network service be a determinant to predict advertising effectiveness. To this end, an advertising effect prediction model for celebrity endorsed advertising was created and its validity was verified through a machine learning method which is a big data analysis technique. Firstly, the celebrity-brand image similarity, which was used as an independent variable, was quantified by the association network theory with social big data, and secondly a multiple regression model which used data representing advertising effects as a dependent variable was repeatedly conducted to generate an advertising effect prediction model. The accuracy of the prediction model was decided by comparing the prediction results with the survey outcomes. As for a result, it was proved that the validity of the predictive modeling of advertising effects was secured since the classification accuracy of 75%, which is a criterion for judging validity, was shown. This study suggested a new methodological alternative and direction for big data-based modeling research through celebrity-brand image similarity structure based on social network theory, and effect prediction modeling by machine learning.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.363-384
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• 2024

This study was conducted to examine the user's experiences with the public library in Daejeon using big data analysis, focusing on the text mining technique. To know this, first, the overall evaluation and perception of users about the public library in Daejeon were explored by collecting data on social media. Second, through analysis using online news articles, the pending issues that are being discussed socially were identified. As a result of the analysis, the proportion of users with children was first high. Next, it was found that topics through LDA analysis appeared in four categories: 'cultural event/program', 'data use', 'physical environment and facilities', and 'library service'. Finally, it was confirmed that keywords for the additional construction of libraries and complex cultural spaces and the establishment of a library cooperation system appeared at the core in the news article data. Based on this, it was proposed to build a library in consideration of regional balance and to create a social parenting community network through business agreements with childcare and childcare institutions. This will contribute to identifying the policy and social trends of public libraries in Daejeon and implementing data-based public library operations that reflect local community demands.

• The Research Journal of the Costume Culture
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• v.25 no.1
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• pp.1-15
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• 2017

Social commerce is a kind of internet shopping mall in which consumers purchase the products with other consumers through mutual interactions including the development of SNS(social network service). Social commerce has expanded rapidly as a mainstream online shopping mall over the past five years driving consumers to purchase more fashion products providing the cheaper prices than open market internet shopping mall. The purpose of this study is to identify the important parameters of social commerce characteristics and consumer characteristics that affect repurchase intention and word-of-mouth intention. A 221 survey questionnaire was distributed to men in their 20's and 30's who live in Seoul metropolitan area. The data were analyzed utilizing Cronbach's ${\alpha}$, factor analysis, and regression analysis using the SPSS 18.0 program. The results revealed, first, that in terms of social commerce characteristics, three variables(website reputation, interactivity, and product scarcity) influenced repurchase intention. Among them, website reputation identified as the most important factor influencing repurchase intention and word-of-mouth intention. Second, with regard to consumer characteristics, interest and a tendency toward impulse buying affected the repurchase intention, and interest and internet shopping experience have influenced the word-of-mouth intention. Among three variables interest in social commerce identified as the key factor affecting both repurchase intention and word-of-mouth intention. The results of the study provide the practical implications and suggest the business strategies to enhance social commerce in the future by identifying the key social commerce characteristics and consumer characteristics that influence male consumers' buying behaviors.

• Journal of Korean Society of Archives and Records Management
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• v.14 no.2
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• pp.117-128
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• 2014

Social Network Service (SNS) has gained great popularity. Recently, facebook has been used most active. Most of government ministries of Republic of Korea operates facebook. In the case of facebook, it takes advantages of possibilities to inform the direction of policy to aggregate opinions of people for issues. Information gained from facebook has a very important value to be reflected in the policy or to understand public opinions. Long-term storage for this information should be considered. In the case of overseas, tools for long-term storage of documentary facebook have recently been developed. However, it does not save all the data in facebook and ignores the principles for long-term storage. These tools are limited to a simple backup. Therefore, this study aims to investigate how to reproduce, store and collect facebook page records of the government ministries.

• Information Systems Review
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• v.14 no.1
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• pp.21-35
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• 2012

The rapid growth and popularity of Twitter have been one of the most influential phenomena in the era of social network system and the mobile internet, which also opens up opportunities for new business strategies; in particular, PR and marketing area. This study analyzed use of Twitter in terms of user characteristics and message attributes. Actual field data from the Twitter for PR and Marketing of a representative Korean IT company (Company "K") was used for this analysis. Research findings show that overall corporate twitter users show passive attitude in retweet behavior. Also, users who have relatively small network size (less than 1,000) are more active in retweet than power twitterians that have big network size(over than 10,000). It is showed that the rate of retweet is higher in the order of recruiting, promotional event, IT information, and general PR message. In the conclusion section, practical implications based on the research finding are thoroughly discussed.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.395-404
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• 2015

This study is exploring objective awareness of literary therapy by consideration of popular perception about literary therapy through analysis of big data. The purpose of this study is the deduction of meaning information through analysis in the viewpoint of big data at online social network service(SNS) about 'literary therapy'. Accordingly, the main way of research became content analysis of keyword linked to literary therapy by utilizing opinion mining method related to text mining. The study mainly grasped 'literary therapy' and analyzed 'bibliotherapy' comparatively. The period of study was from Oct. 10th to Nov. 10th, 2014(during 30 days), and SNS such as blog or twitter became the subject of search. Through the result of study analysis, the conclusion that the spread of literary therapeutic prospect, structural harmony of literary therapeutic field, and the solidity of perceptional axis about literary therapy are needed can be drawn. This study is worthwhile because it can investigate popular awareness about literary therapy and can suggest alternative for invigoration of literary therapy.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.699-706
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• 2021

This study aims to identify the effects of factors on aggression in adolescents, focusing on the individual, parent, friend factors and SNS usage. In particular, this study is to provide a basis for easing aggression in adolescence by considering the emotional relationship of parents and friends. This study analyzed frequency, t-test, one-way batch distribution analysis(ANOVA), and multi-linear regression, using the data from the 7th year of the Korean Children and Youth Panel Survey. As a result, adolescents who frequently use SNS are more aggressive than adolescents who use less. Among the parental factors, the more abuse and excessive interference were found to be more aggressive, and the higher the coach, the lower the aggressiveness. Furthermore, among the friend factors, it has been shown that the higher the alienation, the more aggressive adolescents are. In order to reduce aggression among adolescents, it is necessary to prepare an integrated program considering the emotional relationship of parents and friends, who are the most influential neighbors, rather than simply restricting the use of SNS.

• Journal of Nutrition and Health
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• v.56 no.5
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• pp.573-588
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• 2023

Purpose: This study aimed to examine social networking service (SNS) use and recommended information utilization (SURU) according to the food-related lifestyles (FRLs) of consumers and analyze how the interaction between the FRL and SURU affects the practice of eating alone and visiting restaurants. Methods: Data on 4,624 adults in their 20s to 50s were collected from the 2021 Consumer Behavior Survey for Food. Statistical methods included factor analysis, K-means cluster analysis, the complex samples general linear model, the complex samples Rao-Scott χ² test, and the general linear model. Results: The following three factors were extracted from the FRL data: Convenience pursuit, rational consumption pursuit, and gastronomy pursuit, and the subjects were classified into three groups, namely the rational consumption, convenient gastronomy, and smart gourmet groups. An examination of the difference in SURU according to the FRL showed that the smart gourmet group had the highest score. The result of analyzing the effects of the FRL and SURU on eating alone revealed that both the main effect and the interaction effect were significant (p < 0.01, p < 0.001). The higher the SURU, the higher the frequency of eating alone in the convenience pursuit, and gastronomy pursuit groups. The main and interaction effects of the FRL and SURU on the frequency of eating out were also significant (p < 0.01, p < 0.001). In all the FRL groups, the higher the SURU level, the higher the frequency of visiting restaurants. Specifically, the two groups with convenience and gastronomic tendencies showed a steeper increase. Conclusion: This study provides important basic data for research on consumer behavior related to food SNS, market segmentation of restaurant consumers, and development of marketing strategies using SNS in the future.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.