• 한국정보통신학회논문지
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• 제25권9호
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• pp.1199-1205
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• 2021

빅데이터 시대에는 단순히 데이터베이스에 잘 정리된 정형 데이터뿐만 아니라 인터넷, 소셜 네트워크 서비스, 모바일 환경에서 실시간 생성되는 웹 문서, 이메일, 소셜 데이터 등 비정형 빅데이터를 효과적으로 분석하는 것이 매우 중요하다. 빅데이터 분석은 데이터 저장소에 저장된 빅데이터 속에서 의미 있는 새로운 상관관계, 패턴, 추세를 발견하여 새로운 가치를 창출하는 과정이다. 빅데이터 분석 도구인 R 언어를 이용하여 비정형 논문 데이터를 빈도분석을 통해 분석결과를 요약과 시각화하고자 한다. 본 연구에서 사용된 데이터는 한국정보통신학회 학회지 논문 중에서 2021년 1월호-5월호 총 논문 104편을 대상으로 분석하였다. 최종 분석결과 가장 많이 언급된 키워드는 "데이터"가 1,538회로 1위를 차지하였다. 따라서 분석결과를 바탕으로 연구의 한계와 이론적 실무적 시사점을 제시하고자 한다.

• 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 Advanced Culture Technology
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• 제11권4호
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• pp.449-455
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• 2023

The purpose of this study is to explore adolescent depression, increase understanding of social problems, and develop prevention and intervention strategies. As a research method, social big data was used to collect information related to 'youth depression', and related factors were identified through data mining and analysis of related rules. We used 'Sometrend Biz Tool' to collect and clean data from the web and then analyzed data in various languages. The study found that online articles about depression decreased during the school holidays (January to March), then increased from March to the end of June, and then decreased again from July. Therefore, it is important to establish a government-wide depression management monitoring system that can detect risk signs of adolescent depression in real time. In addition, regular stress relief and mental health education are needed during the semester, and measures must be prepared to deal with at-risk youth who share their depressed feelings in cyberspace. Results from these studies can be expected to provide important information in investigating and preventing youth depression and to contribute to policy development and intervention.

• Journal of Sport and Applied Science
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• 제8권1호
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• pp.11-17
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• 2024

Purpose: The purpose of the study was to understand a trend of esports in terms of gamers' and fans' perceptions toward esports using social big data. Research design, data, and methodology: In this study, researchers first selected keywords related to esports. Then a total of 10,138 buzz data created at twitter, Facebook, news media, blogs, café and community between November 10, 2022 and November 19, 2023 were collected and analyzed with 'Textom', a big data solution. Results: The results of this study were as follows. Firstly, the news data's main articles were about competitions hosted by local governments and policies to revitalize the gaming industry. Secondly, As a result of esports analysis using Textom, there was a lot of interest in the adoption of the Hangzhou Asian Games as an official event and various esports competitions. As a result of the sentiment analysis, the positive content was related to the development potential of the esports industry, and the negative content was a discussion about the fundamental problem of whether esports is truly a sport. Thirdly, As a result of analyzing social big data on esports and the Olympics, there was hope that it would be adopted as an official event in the Olympics due to its adoption as an official event in the Hangzhou Asian Games. Conclusions: There was a positive opinion that the adoption of esports as an official Olympic event had positive content that could improve the quality of the game, and a negative opinion that games with actions that violate the Olympic spirit, such as murder and assault, should not be adopted as an official Olympic event. Further implications were discussed.

• 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.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• 한국정보통신학회논문지
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• 제24권2호
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• pp.166-171
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• 2020

데이터를 저장하고 분석하여 새로운 지식을 얻을 수 있는 빅데이터 처리기술은 사회의 여러 분야에서 중요성이 강조되고 있으며 정보통신기술 분야의 핵심 이슈로 부각되면서 관련 기술에 대한 관심이 증가하고 있다. 이러한 빅데이터를 분석할 수 있는 도구인 R은 통계 기반의 정보 분석을 가능하게 하는 언어와 환경이다. 본 논문에서는 이를 이용하여 성경데이터를 분석한다. 성경 중에서 신약성경의 4복음서의 데이터를 분석한다. 먼저 성경데이터를 수집하고 분석을 위한 필터링을 수행한다. 이후 R을 이용하여 어떠한 텍스트가 분포되어 있는지를 빈도 조사를 수행하며 정확한 데이터의 분석을 위해 한 문장에서 나오는 단어들을 쌍으로 표현하고 단어 간의 관계성을 분석하는 소셜 네트워크 분석을 통해 성경을 분석한다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.98-108
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• 2022

In this study, social network analysis was performed to compare and analyze changes in domestic tourism trends before and after the outbreak of COVID-19 in a situation where the damage to the tourism industry due to COVID-19 is increasing. Using Textom, a big data analysis service, data were collected using the keywords "travel destination" and "travel trend" based on the collection period of 2019 and 2020, when the epidemic spread to the world and became chaotic. After extracting a total of 80 key words through text mining, centrality was analyzed using NetDraw of Ucinet6, and clustered into 4 groups through CONCOR analysis. Through this, we compared and analyzed changes in domestic tourism trends before and after the outbreak of COVID-19, and it is judged to provide basic data for tourism marketing strategies and tourism product development in the post-COVID-19.

• 한국컴퓨터정보학회논문지
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• 제25권8호
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• pp.65-72
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• 2020

금융, 통신 등의 응용 분야에서 빅데이터는 광범위하게 활용되고 있으며, 빅데이터 분석 시장은 해마다 크게 성장하고 있다. 이에 반해 소상공인들의 빅데이터 활용 실적은 저조하며, 이는 기존 시스템이 소상공인들의 여건을 충분히 반영하지 못하는 동시에 서비스 이용 가격 역시 높다는 점에 기인한다. 이를 해결하기 위한 노력의 일환으로, 본 논문에서는 소상공인에 특화된 빅데이터 분석 서비스를 제공하는 새로운 플랫폼을 개발, 제안한다. 먼저 소셜 빅데이터 분석과 관련한 기존 연구들을 비교하고, 소상공인의 마케팅을 돕기 위해 필요한 서비스 지표들을 추출한다. 다음으로 도출된 지표들을 구현한 프로토타입 시스템을 소개하고, 구현을 통해 얻어진 시스템 완성에 필요한 기술적인 이슈들을 논의한다.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.378-384
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• 2023

In this study, we explore social awareness, interest, and acceptance of generative AI, including chatGPT, which has revolutionized web search, 30 years after web search was released. For this purpose, we performed a machine learning-based topic modeling analysis based on Korean news big data collected from November 30, 2022, when chatGPT was released, to August 31, 2023. As a result of our research, we have identified seven topics related to chatGPT and generative AI; (1)growth of the high-performance hardware market, (2)service contents using generative AI, (3)technology development competition, (4)human resource development, (5)instructions for use, (6)revitalizing the domestic ecosystem, (7)expectations and concerns. We also explored monthly frequency changes in topics to explore social interest related to chatGPT and Generative AI. Based on our exploration results, we discussed the high social interest and issues regarding generative AI. We expect that the results of this study can be used as a precursor to research that analyzes and predicts the diffusion of innovation in generative AI.