• 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.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.30-37
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• 2018

The social media data and the broadcasting data related to onion as well as agri-food consumer panel data were collected and investigated if the amount of money spent to purchase onion in year 2014 when onion price plunged latest were correlated with the frequencies of onion-related keywords in the social media data and the broadcasting programs because onion price in year 2018 is expected to plunge due to overproduction and there has been needs to analyze impacts of social media and broadcasting program on onion purchase in the previous similar events, and identify potential factors that can promote onion consumption in advance. What we identified from our study include a) broadcasting news programs mentioning words "onion," were correlated with onion purchase with 3 - 6 weeks in advance; b) broadcasting entertainment programs mentioning words "onion and health," were correlated with onion purchase with 11 weeks in advance; c) blog mentioning words "onion and efficacy," were correlated with onion purchase with 5 weeks in advance. Our study provided a case on how social media and broadcasting programs could be analyzed for their effects on consumer purchase behavior using big data collection and analysis in the field of agriculture. We propose to use the findings from the study may be applied to promote onion consumption.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.197-198
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• 2019

본 연구는 청소년들의 SNS 이용동기와 이용실태를 알아보고자 하였다. 연구를 위해 부산에 살고 있는 고등학생들을 대상으로 조사를 실시하였으며, 사회적 동기, 유희적 동기, 기능적 동기, 심리적 동기, 정보공유, 탈출욕구, 교우실현, 전문적 혜택 등에 대한 설문문항을 토대로 설문조사를 진행하였다. 설문 결과, 대부분의 학생들이 페이스북을 사용하고 있었으며, 그 다음으로 인스타그램을 많이 이용하는 것으로 나타났다. 그리고 청소년들은 기능적 동기와 유희적 동기와 교유관계 유지를 위한 동기 때문에 SNS를 이용하고 있는 것으로 나타났다. 현재 청소년들이 SNS를 활발히 활용하고 있고 왜 이용하고 있는지에 대한 동기를 파악하여 청소년들의 심리상태를 점검해 볼 수 있다는 차원에서 연구의 의의가 있다고 볼 수 있다.

• Knowledge Management Research
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• v.15 no.3
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• pp.169-188
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• 2014

Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.89-96
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• 2017

Recently, a number of researches actively focus on the contents and sensitivity of information distributed through SNS as smartphones and SNS gained its popularity. In this paper, we collected online news data about SW education, extracted words after morphological analysis, and analyzed emotions of collected news data by calculating sentimental score of each news datum. Also, the accuracy of the calculated sentimental score was examined. As a result, the number of news related to 'SW education' in the collection period was about 189 per month, and the average of sentimental score was 0.7, which signifies the news related to 'SW education' was emotionally positive. We were positive about the importance of SW education and the policy implementation, but there were negative views on the specific method for the realization. That is, a lack of SW education environment and its education method, a problem related to improvement of SW developers and improvement of their labor conditions, and increase of private education in coding were the factors for the negative viewers.

• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.116-126
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• 2019

The production volume and amount of non-timber forest products in Korea has been on the increase for the past five years. In particular, the production amount of wild vegetables (edible mountain plants) is approximately KRW 400 billion as of 2017, accounting for 14 % of the total production amount of non-timber forest products. Among wild vegetables, especially the production volumes and amounts of bracken, saw-wort (Saussurea), and thistle have grown steadily. Nevertheless, severe price competition with cheap imports and little changes in the pattern of wild vegetable consumption may negatively affect the prices of domestic wild vegetables. This, in turn, can decrease the overall consumption of wild vegetables. Recently, however, consumers have preferred healthy food with increases in their income and interest in health. Therefore, now is a crucial time for the wild vegetable market. Accordingly, this study analyzed consumers' purchase and consumption behavior related to wild vegetables through a consumer survey to contribute to establishing various strategies and policies for promoting the consumption of these vegetables. Also, this study identified consumers' awareness and intention regarding wild vegetables by analyzing social big data. Different from previous studies, this study investigated consumers' awareness and intention by analyzing SNS social big data, as well as conducting a survey. The results of the study will help prioritize strategies and policies for boosting the consumption of wild vegetables.

• Journal of Information Technology Services
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• v.22 no.3
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• pp.119-139
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• 2023

Generation MZ has emerged as a significant consumer segment and trendsetter in the fashion market of South Korea. Fashion designer brands have become popular among this generation by offering a range of fashion content on social network services (SNS) based on fresh and trendy designs. Despite the growing market share of fashion designer brands in the industry, previous research has mainly focused on brand personality in line with the characteristics of traditional fashion brands. Therefore, this study aims to derive brand personality and SNS characteristics of fashion designer brands based on previous research and investigate the influence of these factors on brand preference and behavioral intention. Moreover, it examines how this influencing mechanism fluctuates based on the consumer type (i.e., innovative type vs. price-sensitive type). Based on an online survey of 256 Korean adults with experience in fashion designer brands, this study identified the influencing mechanisms on purchase intention and word-of-mouth intention. This study contributes to empirical investigations of consumer brand preference and behavior intention in fashion designer brands through the brand equity model. It also offers insight into developing a segmented brand strategy by considering the variations in the influence mechanism of behavioral intention across different consumer types.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.159-168
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• 2012

Relation in the new IT environment, such as the SNS, Cloud, Web3.0, has become an important factor. And these relations generate a transaction. However, existing relational database and graph database does not processe graph structure representing the relationships and transactions. This paper, we propose the technique that can be processed concurrently graph structures and transactions in a scalable complex network system. The proposed technique simultaneously save and navigate graph structures and transactions using the Topic Maps data model. Topic Maps is one of ontology language to implement the semantic web(Web 3.0). It has been used as the navigator of the information through the association of the information resources. In this paper, the architecture of the proposed technique was implemented and design using Cassandra - one of column type NoSQL. It is to ensure that can handle up to Big Data-level data using distributed processing. Finally, the experiments showed about the process of storage and query about typical RDBMS Oracle and the proposed technique to the same data source and the same questions. It can show that is expressed by the relationship without the 'join' enough alternative to the role of the RDBMS.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.385-392
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• 2018

Iksan is one of medium city in Jellabukdo, South Korea. It has a favorable natural environment for the specialization potential of natural industries and development projects. In addition, it has various historical and cultural resources including Mireuksajji, and KTX Honam line which has been opened for a representative feature as transport city. However, it faces week connection with neighboring cities and large scale of development in neighboring areas, especially in Jeonju and Gunsan. In this paper, we try to classify the urban image assets of Iksan as 'Iksan Station' and 'ktx' on keywords and analyze the possibility of being a center of transportation and logistics through big data analysis extracted from SNS and website. In comparison with Gwangju Songjeong, KTX Honam line station, which has been developed with similar regional characteristics, it is aimed to establish the basis of improvement and establishment of urban image of Iksan city in the future.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.