• The Journal of the Korea Contents Association
• /
• v.22 no.10
• /
• pp.11-21
• /
• 2022

All-solid-state batteries are one of the promising candidates for next-generation batteries and are drawing attention as a key component that will lead the future electric vehicle industry. This study analyzes 10,280 comments on Reddit, which is a global social media, in order to identify policy issues and public interest related to all-solid-state batteries from 2016 to 2021. Text mining such as frequency analysis, association rule analysis, and topic modeling, and sentiment analysis are applied to the collected global data to grasp global trends, compare them with the South Korean government's all-solid-state battery development strategy, and suggest policy directions for its national research and development. As a result, the overall sentiment toward all-solid-state battery issues was positive with 50.5% positive and 39.5% negative comments. In addition, as a result of analyzing detailed emotions, it was found that the public had trust and expectation for all-solid-state batteries. However, feelings of concern about unresolved problems coexisted. This study has an academic and practical contribution in that it presented a text mining analysis method for deriving key issues related to all-solid-state batteries, and a more comprehensive trend analysis by employing both a top-down approach based on government policy analysis and a bottom-up approach that analyzes public perception.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.166-171
• /
• 2020

Big data processing technology that can store and analyze data and obtain new knowledge has been adjusted for importance in many fields of the society. Big data is emerging as an important problem in the field of information and communication technology, but the mind of continuous technology is rising. the R, a tool that can analyze big data, is a language and environment that enables information analysis of statistical bases. In this paper, we use this to analyze the Bible data. We analyze the four Gospels of the New Testament in the Bible. We collect the Bible data and perform filtering for analysis. The R is used to investigate the frequency of what text is distributed and analyze the Bible through social network analysis, in which words from a sentence are paired and analyzed between words for accurate data analysis.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06b
• /
• pp.411-413
• /
• 2012

R&D 기획은 연구 개발 전주기에 있어 수행, 평가에 앞서 필수적으로 선행되어야 하는 행위이다. 그렇지만, 아직까지 R&D 기획에서 무엇을 다루어야 하는지에 대한 원칙, 사례만 존재하고 이를 통합 시스템화하지 못하고 있어, 대부분의 연구자들은 논문, 특허, 웹을 포함한 다양한 자원들로부터 정보를 수집, 취합, 분석하는 데 많은 시간을 뺏기고 있는 형편이다. 이 문제를 해결하기 위해서 본 연구 이전에 시맨틱 기술과 텍스트 마이닝 기술을 이용하여 R&D 전략 수립을 지원하고자 InSciTe, InSciTe Advanced 등이 개발되었지만, R&D 기획이라는 궁극적 목적 내에서도 사용자마다 다양한 시나리오들을 분별하여 지원하기에는 어려움이 많았다. 이에 본 연구는 사용자 적응적 가이드 방식을 통합적으로 적용하여 실시간으로 변화하는 사용자 관심을 파악하고, 대표적 시나리오들에 맞추어 사용자가 목적을 효과적으로 달성할 수 있도록 지원하는 데 초점을 맞추고자 한다. 먼저 사용자 적응적 가이드 방식의 R&D 기획 시스템 구축을 위해 필요한 요구 사항들과 이들을 효과적으로 시스템에 반영하기 위한 온톨로지, 웹 서비스 중심의 설계 원칙들을 제시함으로써 R&D 기획뿐만 아니라 타 분야에서도 유연하게 적용할 수 있는 기반을 제공한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.1039-1040
• /
• 2015

최근 IoT 및 인공지능 기술을 활용한 상황 정보 예측 서비스가 각광을 받고 있다. 본 논문에서는 특정 메타 데이터(Meta Data)로부터 입력되는 정보를 기반으로 상황 정보 분석 및 예측하는 프로세스를 제안한다. 주성분 분석 및 데이터의 집단화(Corpus), 문서 매트릭스(Document Matrix), 단어 빈도수(Frequency)에 따른 데이터 전처리 과정을 통해 상황정보 데이터를 확보한다. 또한 연관 규칙분석을 통해 분류된 데이터의 연관성을 분석하여 예측 데이터의 연관성을 확보한다. 제안하는 상황정보 분석 및 예측 모델은 R을 적용하여 설계한다.

• Fashion & Textile Research Journal
• /
• v.22 no.5
• /
• pp.551-560
• /
• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.470-476
• /
• 2015

Big data has been used in diverse areas. For example, in computer science and sociology, there is a difference in their issues to approach big data, but they have same usage to analyze big data and imply the analysis result. So the meaningful analysis and implication of big data are needed in most areas. Statistics and machine learning provide various methods for big data analysis. In this paper, we study a process for big data analysis, and propose an efficient methodology of entire process from collecting big data to implying the result of big data analysis. In addition, patent documents have the characteristics of big data, we propose an approach to apply big data analysis to patent data, and imply the result of patent big data to build R&D strategy. To illustrate how to use our proposed methodology for real problem, we perform a case study using applied and registered patent documents retrieved from the patent databases in the world.

• The Journal of Society for e-Business Studies
• /
• v.24 no.2
• /
• pp.71-89
• /
• 2019

In order to come up with satisfying product and improvement, firms use traditional marketing research methods to obtain consumers' opinions and further try to reflect them. Recently, gathering data from consumer communication platforms like internet and SNS has become popular methods. Meanwhile, with the development of information technology, mobile companies are launching new digital products for children to protect them from harmful content and provide them with necessary functions and information. Among these digital products, Kids Phone, which is a wearable device with safe functions that enable parents to learn childern's location. Kids phone is relatively cheaper and simpler than smartphone but it is noted that there are several problems such as some useless functions and frequent breakdowns. This study analyzes the reviews of Kids phones from domestic mobile companies, identifies the characteristics, strengths and weaknesses of the products, proposes improvement methods strategies for devices and services through SNS consumer analysis. In order to do that customer review data from online shopping malls was gathered and was further analyzed through text mining methods such as TF/IDF, Sentiment Analysis, and network analysis. Customer review data was gathered through crawling Online shopping Mall and Naver Blog/$Caf\acute{e}$. Data analysis and visualization was done using 'R', 'Textom', and 'Python'. Such analysis allowed us to figure out main issues and recent trends regarding kids phones and to suggest possible service improvement strategies based on sentiment analysis.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.spc
• /
• pp.1-8
• /
• 2023

To investigate the national science and technology policy direction in the field of construction machinery, an analysis was conducted on projects selected as national research and development (R&D) initiatives by the government. Assuming that the project titles contain key keywords, text mining was employed to substantiate this assumption. Project information data spanning nine years from 2014 to 2022 was collected through the National Science & Technology Information Service (NTIS). To observe changes over time, the years were divided into three-year sections. To analyze research trends efficiently, keywords were categorized into groups: 'equipment,' 'smart,' and 'eco-friendly.' Based on the collected data, keyword frequency analysis, N-gram analysis, and topic modeling were performed. The research findings indicate that domestic government R&D in the construction machinery field primarily focuses on smart-related research and development. Specifically, investments in monitoring systems and autonomous operation technologies are increasing. This study holds significance in analyzing objective research trends through the utilization of big data analysis techniques and is expected to contribute to future research and development planning, strategic formulation, and project management.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.2
• /
• pp.164-172
• /
• 2018

This paper examines the relevance of pre-production and storytelling in big data analysis and, focusing on JTBC's Man to Man series, looks at how the drama's storytelling should be structured. In this study, we conducted text mining on blogs focused on a particular topic to read the viewer's thoughts on pre-produced dramas and on 67 blogs written about Pre-Production Dramas from 2016.12.15 to 2017.12.15. Also, we conducted sentiment analysis about the Man to Man series, which is not only a pre-production drama, but also has storytelling issues. The blog text extraction and text mining were analyzed using the OutWit Hub and the R, and the tools.provided by social metrics were used to make sentiment analyses of the larger data. Sentiment analysis revealed that the viewers of the Man to Man series did not agree with the romance between Kim Sul-woo and Cha Do-ha, due to the lack of reality in the female characters. Therefore, it was concluded that it is crucial to increase the reality of the characters in order to increase the audience's empathy. These studies will continue to be necessary, because they will form the basis for digitally driven storytelling studies and will provide valuable materials for conducting predictions and instructions in the cultural content industry.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.38 no.2
• /
• pp.17-30
• /
• 2015

Technology-oriented national R&D programs produce intellectual property as their final result. Patents, as typical industrial intellectual property, are therefore considered an important factor when evaluating the outcome of R&D programs. Among the main components of patent evaluation, in particular, the patent right quality is a key component constituting patent value, together with marketability and usability. Current approaches for patent right quality evaluation rely mostly on intrinsic knowledge of patent attorneys, and the recent rapid increase of national R&D patents is making expert-based evaluation costly and time-consuming. Therefore, this study defines a hierarchy of patent right quality and then proposes how to quantify the evaluation process of patent right quality by combining text mining and regression analysis. This study will contribute to understanding of the systemic view of the patent right quality evaluation, as well as be an efficient aid for evaluating patents in R&D program assessment processes.