• 한국정보시스템학회지:정보시스템연구
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• 제32권2호
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.

• 아태비즈니스연구
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• 제9권3호
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• pp.51-69
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• 2018

This study intends to investigate the management efficiency of social enterprises according to types based on the portion of the budget for employing disadvantaged social groups, in the region of Gyeonggi-do. Based on the performance list disclosed at Korea Social Enterprise Promotion Agency's website, 126 social enterprises certified during a period of five years from 2013 to 2017, 126 enterprises were analyzed by using data envelopment analysis (DEA) models comparing five types of the enterprises. The types was mainly identified by the job security of disadvantaged social groups. As for measurement variables, the input components included average wage, support fund, and the number of non-vulnerable employees and the number of vulnerable employees, sales, and net income were selected as output variables. In conclusion, the efficiency of Gyeonggi-do social enterprises decreased every year, and thus it is urgent to improve their efficiency, and priority should be given to the employment of vulnerable social groups, which both the job opportunity providing-type and the social service providing-type showed the highest performance.

• 한국컴퓨터정보학회논문지
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• 제26권8호
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• pp.39-46
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• 2021

프랜차이즈 스토어를 대상으로 소셜 빅데이터 분석을 수행할 경우, 프랜차이즈에 속한 여러 분점의 리뷰들이 함께 수집될 수 있어 분석 결과가 왜곡될 수 있다. 이 경우 분석 정확도를 높이기 위해서는 분석 대상이 아닌 타 분점의 리뷰들을 적절히 필터링할 수 있어야 한다. 본 논문에서는 프랜차이즈 스토어들의 특성을 반영한 소셜 빅데이터 분석 방법을 제안한다. 제안 방법은 검색어 설정 방법과 리뷰 필터링 방법을 포함한다. 검색어 설정을 위해, 소상공인진흥공단에서 제공하는 공공데이터를 기반으로 검색에 필요한 지역명을 추출한다. 그리고 리뷰 필터링을 위해, 네이버 및 카카오 등에서 제공하는 검색 API를 이용하여 프랜차이즈 분점 정보를 알아내고, 분석 대상이 아닌 타 분점의 리뷰들을 필터링하는데 이용한다. 제안 방법의 검증을 위해 온라인에서 수집된 실제 리뷰를 대상으로 실험을 수행하였으며, 제안 방법의 리뷰 필터링 정확도는 평균 93.6%로 조사되었다.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.358-368
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• 2022

Social media is a window for everyone, individuals, communities, and companies to spread ideas and promote trends and products. With these opportunities, challenges and problems related to security, privacy and rights arose. Also, the data accumulated from social media has become a fertile source for many analytics, inference, and experimentation with new technologies in the field of data science. In this chapter, emphasis will be given to methods of trend analysis, especially ensemble learning methods. Ensemble learning methods embrace the concept of cooperation between different learning methods rather than competition between them. Therefore, in this chapter, we will discuss the most important trends in ensemble learning and their applications in analysing social media data and anticipating the most important future trends.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.603-608
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• 2005

The purpose of this qualitative case study is to understand how the idea of data view and information graphics is used in the social studios middle school textbooks. Data were collected through national curriculum documents and social studies middle textbooks for 7-9 grades. We set up three questions for this studies; what kinds of information graphics are used in the textbooks, how the graphics are organized in the social studies middle school, and how the 7th social studies curriculum is related with the 7th national mathematics curriculum. Through the data analysis, we found that 1) Photographs, illustrations, information maps, etc., are used and frequencies of their usages are in descending order, 2) double lines graphs, circle graphs, and stripe graphs nip often adopted for the comparison of populations, 3) the relation of the two subjects curricula is not so good, especially in the curriculum steps of information mads scatter diagrams, and comparison of populations. Finally we suggest that new web site of data view or information graphics be provided for two curricula, workshop of information graphics are needed for social studies teachers.

• 한국콘텐츠학회논문지
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• 제20권2호
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• pp.96-104
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• 2020

온라인 쇼핑몰의 활성화로 소비자들의 소비 활동이 활발해짐에 따라 기업들은 매출 증대를 위해 소비자의 상품 트렌드 분석을 수행하고 있다. 기존의 상품 트렌드 분석 기법들은 온라인 쇼핑몰 사용자의 활동만을 고려하여 분석하기 때문에 구매 이력이 없거나 새로운 상품에 대한 트렌드를 파악하기 어렵다. 본 논문에서는 쇼핑몰에서 사용자의 트렌드와 잠재적 고객의 트렌드를 분석하기 위해 온라인 쇼핑몰 데이터와 소셜 네트워크 데이터를 결합한 트렌드 분석 기법을 제안한다. 제안하는 기법은 서비스 내 데이터 분석을 위해 사용자의 활동로그를 분석하고 활동 로그가 없는 잠재적인 사용자들의 관심도를 반영하기 위해 소셜 네트워크 데이터에서 단어 집합 추출을 통해 생성한 핫 토픽을 결합하였다. 최종적으로 상품 지수와 소셜 네트워크에서의 언급수를 활용하여 시간에 따른 상품 트렌드 변화를 탐지한다. 소셜 네트워크 데이터를 활용한 성능 평가를 통해 제안하는 기법의 우수성을 입증한다.

• 한국유기농업학회지
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• 제29권1호
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• pp.41-50
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• 2021

This paper analyzes the efficiency of social enterprises by analyzing bootstrapping data envelopment analysis. Unlike the definitive DEA model, we analyze the confidence intervals of efficiency estimates through the DEA model, which takes into account stochastic factors. Major analysis results are summarized as follows: First, the results of the bootstrapping DEA analysis of social enterprises estimated that the technical efficiency was 0.459 and the 95% confidence interval was 0.389 to 0.601. Second, the number of inefficient social enterprises with efficiency values of less than 0.5 was found to be 15 (55.56%) in technical efficiency, 5 (18.52%) in pure technical efficiency, and 8 (29.63%) in scale efficiency. It can be seen that a significant number of social enterprises are operating in an inefficient state. Third, looking at the returns of scale of social enterprises, 25 (67.57%) are currently in the increasing returns of scale, 10 (27.02%) are in the constant returns of scale, and 2 (5.41%) are in decreasing returns of scale. In other words, it can be seen that social enterprises are under-invested in terms of input factors.

• Human Ecology Research
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• 제59권1호
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• pp.23-43
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• 2021

This study looked through the text mining analysis to check the status of the virtual assistant service, and explore the needs of consumers, and present consumer-oriented directions. Trendup 4.0 was used to analyze the keywords of AI services in Online News and social media from 2016 to 2020. The R program was used to collect consumer comment data and implement Topic Modeling analysis. According to the analysis, the number of mentions of AI services in mass media and social media has steadily increased. The Sentimental Analysis showed consumers were feeling positive about AI services in terms of useful and convenient functional and emotional aspects such as pleasure and interest. However, consumers were also experiencing complexity and difficulty with AI services and had concerns and fears about the use of AI services in the early stages of their introduction. The results of the consumer review analysis showed that there were topics(Technical Requirements) related to technology and the access process for the AI services to be provided, and topics (Consumer Request) expressed negative feelings about AI services, and topics(Consumer Life Support Area) about specific functions in the use of AI services. Text mining analysis enable this study to confirm consumer expectations or concerns about AI service, and to examine areas of service support that consumers experienced. The review data on each platform also revealed that the potential needs of consumers could be met by expanding the scope of support services and applying platform-specific strengths to provide differentiated services.

• International Journal of Advanced Culture Technology
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• 제8권3호
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• pp.133-139
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• 2020

The purpose of this study is to contribute to the provision of basic data for psychological quarantine policy and counseling by examining the public perception of the "corona blue" phenomenon through analysis of social media big data. To do this, key words related to the word 'Corona Blue' were derived and analyzed using the big data analysis program 'Textom'. As a result of the analysis, words such as 'Corona 19', 'depression', 'problem' and 'overcome' were derived as key words. For the analysis results,"pride and awarenes as the public perception of Corona 19", "depression and anxiety as a group trauma as the corona blue phenomenon", "spreading a psychological quarantine culture and demanding social healing as the perception of overcoming corona Blue," and "hope for return to daily life and changes in daily life as the perception of post corona" were discussed. In conclusion, we have identified the need for active psychological support from the community By revealing that Corona Blue is a depression as a group trauma. At this time, it is confirmed that it is necessary to prioritize social healing and psychological quarantine for the main risk groups such as youth or the vulnerable, who are the socially weak.

• 복식문화연구
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• 제32권1호
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• pp.38-57
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• 2024

This study utilizes social big data to investigate the factors influencing the awareness, attitude, and behavior toward vegan fashion consumption among global and Korean consumers. Social media posts containing the keyword "vegan fashion" were gathered, and meaningful discourse patterns were identified using semantic network analysis and sentiment analysis. The study revealed that diverse factors guide the purchase of vegan fashion products within global consumer groups, while among Korean consumers, the predominant discourse involved the concepts of veganism and ethics, indicating a heightened awareness of vegan fashion. The research then delved into the factors underpinning awareness (comprehension of animal exploitation, environmental concerns, and alternative materials), attitudes (both positive and negative), and behaviors (exploration, rejection, advocacy, purchase decisions, recommendations, utilization, and disposal). Global consumers placed great significance on product-related information, whereas Korean consumers prioritized ethical integrity and reasonable pricing. In addition, environmental issues stemming from synthetic fibers emerged as a significant factor influencing the awareness, attitude, and behavior regarding vegan fashion consumption. Further, this study confirmed the potential presence of cultural disparities influencing overall awareness, attitude, and behavior concerning the acceptance of vegan fashion, and offers insights into vegan fashion marketing strategies tailored to specific cultures, aiming to provide vegan fashion companies and brands with a deeper understanding of their consumer base.