• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.553-564
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• 2014

In this paper, we propose the system for automatic generation of issue analysis report based on social big data mining, with the purpose of resolving three problems of the previous technologies in a social media analysis and analytic report generation. Three problems are the isolation of analysis, the subjectivity of experts and the closure of information attributable to a high price. The system is comprised of the natural language query analysis, the issue analysis, the social big data analysis, the social big data correlation analysis and the automatic report generation. For the evaluation of report usefulness, we used a Likert scale and made two experts of big data analysis evaluate. The result shows that the quality of report is comparatively useful and reliable. Because of a low price of the report generation, the correlation analysis of social big data and the objectivity of social big data analysis, the proposed system will lead us to the popularization of social big data analysis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.207-210
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• 2018

인플루언서(Influencer)란 소셜 미디어에서 유난히 많은 영향력과 파급효과를 가지고 오는 사람들을 말하며, 이들이 만들어내는 콘텐츠는 이제는 자신들의 브랜딩을 넘어선 커머스(Commerce) 효과를 발휘하고 있다. 본 논문에서는 소셜 웹 그리고 공공데이터를 중심으로 뷰티 빅데이터와 방송 콘텐츠 빅데이터를 수집하고 분석하여 상호 상관성에 기반하여 화장품 관련 기업에서 CRM(Customer Relation Management), PLM(Product Lifecycle Management, SCM(Supply Chain Management System) 등의 경영정보시스템과 연계한 뷰티 분야에 최적화된 통합 경영정보시스템을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1199-1205
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• 2021

In the era of big data, not only structured data well organized in databases, but also the Internet, social network services, it is very important to effectively analyze unstructured big data such as web documents, e-mails, and social data generated in real time in mobile environment. Big data analysis is the process of creating new value by discovering meaningful new correlations, patterns, and trends in big data stored in data storage. We intend to summarize and visualize the analysis results through frequency analysis of unstructured article data using R language, a big data analysis tool. The data used in this study was analyzed for total 104 papers in the Mon-May 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 1,538 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.527-538
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• 2014

The demand prediction is a critical issue for the film industry. As the social media, such as Twitter and Facebook, gains momentum of late, considerable efforts are being dedicated to prediction and analysis of hit movies based on unstructured text data. For prediction of trends found in commercially successful films, the correlations between the amount of data and hit movies may be analyzed by estimating the data variation by period while opinion mining that assigns sentiment polarity score to data may be employed. However, it is not possible to understand why the audience chooses a certain movie or which attribute of a movie is preferred by using such a quantitative approach. This has limited the efforts to identify factors driving a movie's commercial success. In this regard, this study aims to investigate a movie's attributes that reflect the interests of the audience. This would be done by extracting topic keywords that represent the contents of Twits through frequency measurement based on the collected Twitter data while analyzing responses displayed by the audience. The objective is to propose factors driving a movie's commercial success.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.1-9
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• 2019

The purposes of the study are to investigate Koreans' perception about Vietnam by analyzing social big data and to seek changing direction in perception. For the purposes, the texts about Vietnam in Naver Blog and Twitter and the number of search and click for Vietnam in Naver were analyzed by Social Metrics of Daum Soft and Datalab of Naver. The study also analyzed the annual change of their interest in Vietnam based on social media. The results showed that Koreans still remember the Vietnam war, have a positive emotion toward Vietnam, and view Vietnam as a country where we can gain mutual benefit by exchange. The findings also indicated that Koreans perceive Vietnam as a favorite tourist spot regardless of age. Meanwhile, children under 12 showed a different pattern of an annual change in perception. It might be a positive sign that Koreans' interest region toward Vietnam would be diversified because children under 12 would be the central axis of cultural contents.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.167-181
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• 2023

This study utilized TEXTOM, a social network analysis program to analyze changes in current tourism behavior after travel restrictions were eased after the outbreak of COVID-19. Data on the keywords 'domestic travel' and 'overseas travel' were collected from blogs, cafes, and news provided by Naver, Google, and Daum. The collection period was set from April to December 2022 when social distancing was lifted, and 2019 and 2020 were each set as one year and compared and analyzed with 2022. A total of 80 key words were extracted through text mining and centrality analysis was performed using NetDraw. Finally, through the CONCOR, the correlated keywords were clustered into 4. As a result of the study, tourism behavior in 2022 shows tourism recovery before the outbreak of COVID-19, segmentation of travel based on each person's preferred theme, prioritization of each country's corona mitigation policy, and then selecting a tourist destination. It is expected to provide basic data for the development of tourism marketing strategies and tourism products for the newly emerging tourism ecosystem after COVID-19.