• Journal of Nutrition and Health
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• v.56 no.3
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• pp.330-347
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• 2023

Purpose: This study identified words closely associated with the keyword "dietary supplement" (DS) using big data in Korean social media and investigated consumer perceptions and trends related to DSs before (2019) and after the coronavirus disease 2019 (COVID-19) pandemic (2021). Methods: A total of 37,313 keywords were found for the 2019 period, and 35,336 keywords were found for the 2021 period using blogs and cafes on Daum and Naver. Results were derived by text mining, semantic networking, network visualization analysis, and sentiment analysis. Results: The DS-related keywords that frequently appeared before and after COVID-19 were "recommend", "vitamin", "health", "children", "multiple", and "lactobacillus". "Calcium", "lutein", "skin", and "immunity" also had high frequency-inverse document frequency (TF-IDF) values. These keywords imply a keen interest in DSs among Korean consumers. Big data results also reflected social phenomena related to DSs; for example, "baby" and "pregnant woman" had lower TD-IDF values after the pandemic, suggesting lower marriage and birth rates but higher values for "joint", indicating reduced physical activity. A network centered on vitamins and health care was produced by semantic network analysis in 2019. In 2021, values were highest for deficiency and need, indicating that individuals were searching for DSs after the COVID-19 pandemic due to a lack an awareness of the need for adequate nutrient intake. Before the pandemic, DSs and vitamins were associated with healthcare and life cycle-related topics, such as pregnancy, but after the COVID-19 pandemic, consumer interests changed to disease prevention and treatment. Conclusion: This study provides meaningful clues regarding consumer perceptions and trends related to DSs before and after the COVID-19 pandemic and fundamental data on the effect of the pandemic on consumer interest in dietary supplements.

• Journal of Distribution Science
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• v.12 no.6
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• pp.81-88
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• 2014

Purpose - Entrepreneurship promotion is emerging as an important economic growth agenda. However, in Korea, entrepreneurship has weakened because of the collapse of the venture bubbles of the 2000s and the global economic recession in 2008, which have induced the business community to choose stability over risk. The Korean government has been implementing several support projects to inspire and promote youth entrepreneurship through various means including financial assistance; however, the perpetuation rate of young entrepreneurship is still low as compared to advanced economies such as the US and EU. This case study focuses on the Youth Start-Up Business Support Program of the Small & Medium Business Corporation, and explores practical alternatives. Further, it aims to suggest managerial factors and a conceptual model for change management factors affecting the business performance creation of a startup company, based on the Small and medium Business Corporation's young venture startup fund. Research design, data, and methodology - Many studies examine the current progress and issues of startup firms, for example, a lack of systematic cultivation of entrepreneurship and startup business training, lack of commercialization funding for youth startup businesses, lack of mentoring, and inadequate infrastructure. From prior research, we address four factors, namely, personal managerial capabilities, innovative business model, sufficient cash flow, and social network, affecting startup companies' business performance. This study involved a sample survey of 200 young entrepreneurs to investigate casual relations between the four factors and business performance. A regression analysis was used to verify the hypotheses. Results - First, in relation to differences in the founder's personal characteristics, age, sales amount, and number of employees significantly impact business performance. Second, regarding the causal relation between the four factors for creating business performance, an innovative business model and social networking have supported the hypotheses, revealing that the more that a start-up founder has an innovative business model and social networking, the more the start-up firms are likely to have better performance (e.g., sales volume, employment, ROE, ROI, etc.). Although the founder's competency and sufficient cash flow have no significant relationship with business performance, the mean value was higher performance for high founder's competency and sufficient cash flow. Conclusions - This study provides basic data on policy support strategies of the Small and Medium Business Corporation, to help young entrepreneurs achieve their start-up business goals. It shows that young entrepreneurship startup firms should strive to explore ideas to satisfy customers' needs, and that changes in customer value and the continuous innovation of business model differentiation are required to actively respond to change management. Moreover, at the infant startup stage, they should activate social network programs to share information, thereby offsetting resource scarcity and managing business risk. Further, the establishment of a long-term vision and the implementation of training programs in related specific fields should be supported to strengthen founders' personal capabilities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.718-727
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• 2011

This research aims to empirically investigate into the relationship between venture entrepreneurs' social network and their social competence to utilize the network, which is known to help overcome the weaknesses of small-and-medium-sized ventures. An analysis was made of SEM set up with 211 entrepreneurs' responses, and the following are the findings from the analysis. First, venture entrepreneurs' networking ability has a significant, strong and positive effect on weak tie of their social network(${\beta}$=.527, C.R.=3.626), strong tie(${\beta}$=.594, C.R.=3.969), and the network centrality(${\beta}$=.418, C.R.=4.884). Second, their social astuteness also has a significant and positive effect on weak tie(${\beta}$=.192, C.R.=1.701), strong tie(${\beta}$=.269, C.R.=2.509) and the network centrality(${\beta}$=.228, p=2.283). Third, their interpersonal influence has a significant but negative effect only on strong tie(${\beta}$=-.264, C.R.=-1.862) and the network centrality(${\beta}$=-.394, C.R.=-2.914). Lastly, their apparent sincerity has no significant effect on the subordinate factors of social network. This research has not just empirically analyzed the relationship between the entrepreneurs' social network and their social competence. But also, results of the research provide practical and detailed information to entrepreneurs of small and medium ventures. Moreover, the research is significant in that it has suggested and empirically analyzed the concept of political competence, which is a concrete substance of social competence, and that it has offered theoretical foundations for future researches, which will tackle the issue of the entrepreneurs' competence in the sphere of entrepreneurship.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.850-854
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• 2017

Public acceptance of nuclear power is important for the government, the major stakeholder of the industry, because consensus is required to drive actions. It is therefore no coincidence that the governments of nations operating nuclear reactors are endeavoring to enhance public acceptance of nuclear power, as better acceptance allows stable power generation and peaceful processing of nuclear wastes produced from nuclear reactors. Past research, however, has been limited to epistemological measurements using methods such as the Likert scale. In this research, we propose big data analysis as an attractive alternative and attempt to identify the attitudes of the public on nuclear power. Specifically, we used common big data analyses to analyze consumer opinions via SNS (Social Networking Services), using keyword analysis and opinion analysis. The keyword analysis identified the attitudes of the public toward nuclear power. The public felt positive toward nuclear power when Korea successfully exported nuclear reactors to the United Arab Emirates. With the Fukushima accident in 2011 and certain supplier scandals in 2012, however, the image of nuclear power was degraded and the negative image continues. It is recommended that the government focus on developing useful businesses and use cases of nuclear power in order to improve public acceptance.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1125-1133
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• 2014

Recently, there are many researches have been studying for analyzing user interests and emotions based on users profiles and diverse information from Social Network Services (SNSs) due to their popularities. However, most of traditional researches are focusing on their researches based on single resource such as text, image, hash tag, and more, in order to obtain what user emotions are. Hence, this paper propose a method for obtaining user emotional information by analyzing texts and images both from Instagram which is one of the well-known image based SNSs. In order to extract emotional information from given images, we firstly apply GRAB-CUT algorithm to retrieve objects from given images. These retrieved objects will be regenerated by their representative colors, and compared with emotional vocabulary table for extracting which vocabularies are the most appropriate for the given images. Afterward, we will extract emotional vocabularies from text information in the comments for the given images, based on frequencies of adjective words. Finally, we will measure WUP similarities between adjective words and emotional words which extracted from the previous step. We believe that it is possible to obtain more precise user emotional information if we analyzed images and texts both time.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.357-360
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• 2011

최근 인터넷과 통신기술, 특히 모바일과 관련된 기술의 급속한 발전으로 소셜 커뮤니케이션 수단으로 대표되는 SNS(Social Networking Service)가 중요한 이슈로 부각되어지고 있다. SNS 서비스 제공시 중요하게 고려되어져야 할 사항은 정확하고 의미 있는 데이터를 통해서 사용자가 원하고 관심 있는 분야의 정보를 어떻게 제공할 것인가에 초점이 맞춰져 있어야 한다. 그러나 최근 폭발적으로 증가되어지고 있는 소셜 데이터 때문에 사용자는 의미 분석이 정확하게 이루어지지 않은 신뢰성이 결여된 소셜 커뮤니케이션 서비스를 제공받고 있다. 이러한 소셜데이터 분석의 문제점을 해결하기 위해서 본 논문에서는 소셜 네트워크 서비스에 필요한 데이터를 수집하고, 클라우드 컴퓨팅 환경에서 수집된 대용량 SNS 데이터의 의미를 분석 할 수 있는 MapReduce 기반의 분석 모듈의 구조를 제안하였다. 제안한 모듈은 의미 분석에 필요한 소셜 데이터를 수집하는 수집 기능과 수집된 소셜데이터의 의미 분석을 수행하는 분석 기능을 포함하고 있다. 수집 기능은 SNS에서 생성되는 텍스트 형태의 데이터를 수집하고 MapReduce를 통해서 데이터를 분석하기 쉽게 적절한 크기로 생성된 파일을 분할한다. 수집된 소셜 데이터의 의미 분석은 기존 TF-IDF 방식에 개선된 Weighted-MINMAX 적용한 알고리즘을 통해서 구현하였다. 개선된 알고리즘은 단어의 중요도를 평가하고, 중요도가 높은 단어로 구성된 의미정보 제공 서비스를 지원한다. 시스템의 성능 평가를 위해서 노드별 데이터 처리시간과 추출 키워드의 정확도를 측정하였다.

• Journal of Information Science Theory and Practice
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• v.5 no.2
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• pp.48-61
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• 2017

This paper uses 601 cited papers of Pakistani LIS researchers with the purpose to examine the individual performance of these Library and Information Science (LIS) researchers in terms of their research output and its impact on the LIS (national/international) literature by using various bibliometric indicators. A list of 139 authors was compiled with the help of the Library, Information Science, and Technology Abstracts (LISTA) and some other sources. Data were collected from Google Scholar and SPSS version 20 was utilized in order to identify the relationship between self-citations and various performance indices of the authors. The average citations received per paper vary from 1.80 to 10.08. About half of the papers were single-authored whereas less than one-fifth were by three or more authors. The authors who worked in collaboration produced more papers and received more citations. The h-index, g-index, hI-index, hI-norm, and e-index were used to determine the rank for each author. The intra-group citations grid revealed the volume of self-citations and a small group who cite each other more due to close academic and social relationships. The correlations between self-citations and the impact indices used revealed significant differences. Findings are useful for concerned institutions regarding award, promotions, etc. Further, future research should seriously consider the self-citations and social networking of authors while examining their citations-based research performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.123-146
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• 2017

The information that consumers share in the SNS (Social Networking Service) has a great influence on the purchase of consumers. Therefore, it is necessary to pay attention to new research methodology and advertising strategy using Social Big Data. In this context, the purpose of this study is to quantitatively analyze customer value through Social Big Data. In this study, we analyzed the value structure of consumers for the three smartphone brands through text mining and positive/negative image analysis. Analysis result, it was possible to distinguish the emotional aspects (sensitivity) and rational aspects (rationality) for customer value per brand. In the case of the Galaxy S7 and iPhone 6S, emotional aspects were important before the launch, but the rational aspects was important after release date. On the other hand, in the case of the LG G5, emotional aspects were important before and after launch. We can propose two core advertising strategies based on analyzed consumer value. When developing advertising strategy in the case of the Galaxy S7, there is a need to emphasize the rational aspects of product attributes and differentiated functions. In the case of the LG G5, it is necessary to consider the emotional aspects of happiness, excitement, pleasure, and fun that are felt by using products in advertising strategy. As a result, this study will provide a good standard for actual advertising strategy through consumer value analysis. Advertising strategies are primarily driven by intuition or experience. Therefore, it is important to develop advertising strategies by analyzing consumer value through social big data analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2996-3011
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• 2017

With rapid growth of web technology and dissemination of smart devices, social networking service(SNS) is widely used. As a result, huge amount of data are generated from SNS such as Twitter, and sentiment analysis of SNS data is very important for various applications and services. In the existing sentiment analysis based on the $Na{\ddot{i}}ve$ Bayes algorithm, a same number of attributes is usually employed to estimate the weight of each class. Moreover, uncountable and meaningless attributes are included. This results in decreased accuracy of sentiment analysis. In this paper two methods are proposed to resolve these issues, which reflect the difference of the number of positive words and negative words in calculating the weights, and eliminate insignificant words in the feature selection step using Multinomial $Na{\ddot{i}}ve$ Bayes(MNB) algorithm. Performance comparison demonstrates that the proposed scheme significantly increases the accuracy compared to the existing Multivariate Bernoulli $Na{\ddot{i}}ve$ Bayes(BNB) algorithm and MNB scheme.