• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.369-370
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• 2023

새로운 연구를 시작하기 위해서는 과거의 연구 동향을 분석해야 한다. 이를 위해 많은 양의 과거 연구 데이터를 조사해야 하는데, 모든 데이터를 직접 분류하는 방법은 많은 시간과 노력이 필요하기 때문에 비효율적이며, 텍스트 마이닝 기법을 활용한 키워드분석만으로는 연구 동향을 이해하기에 어려움이 존재한다. 이러한 전통적인 키워드 추출 방법의 한계점을 보완하기 위해 본 논문에서는 텍스트 마이닝 기반 GPT 활용 기법을 제안한다. 본 연구에서는 특정 도메인에 대해 텍스트 마이닝 기법을 활용하여 키워드를 추출하고, 이러한 키워드를 해당 도메인의 데이터로 미세 조정(fine-tuning)된 GPT의 입력으로 사용한다. GPT 결과로 생성된 문장을 텍스트 마이닝으로 나온 결과와 비교 분석한다. 이를 통해 연구 분야의 동향 분석을 보다 쉽게 할 수 있을 것으로 기대된다.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.29-40
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• 2022

The aim of this study was to analyze research trends of social service quality from 2007 to 2020 based on text mining and topic modeling. Our focus was to provide foundational materials for social service improvement by discovering the latent meaning of relevant research papers. We collected 97 scholarly articles on social service, social welfare service, and quality from RISS, and implemented two segments of text mining analysis. Our results showed that the first section included 38 papers and the second 59, indicating 6.9 articles annually. Word frequency results demonstrated that the common keywords of both sections were 'service', 'quality', 'social service', 'satisfaction', 'users', 'quality control', 'reuse', 'policy', 'voucher', etc. TF-IDF suggested that 'social service', 'satisfaction', 'users', 'customer satisfaction', 'revisiting', 'voucher', 'quality', 'assisted living facility', 'quality control', 'community service investment business', etc., were represented in both categories. Lastly, topic modeling analysis revealed that the first segment displayed 'types of care services', 'service costs', 'reuse', 'users based', and 'job creation', whereas the second presented 'service quality', 'public value', 'management system of human resources', 'service provision system', and 'service satisfaction'. Future directions of social service quality were discussed based on the results.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.539-549
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• 2024

This study employed text mining techniques like frequency analysis, word clouds, and LDA topic modeling to assess consumer satisfaction and dissatisfaction with Hyundai Motor Company in the Chinese market, compared to brands such as Toyota, Volkswagen, Buick, and Geely. Focusing on compact vehicles from these brands between 2021 and 2023, this study analyzed customer reviews. The results indicated Hyundai Avante's positive factors, including a long wheelbase. However, it also highlighted dissatisfaction aspects like Manipulate, engine performance, trunk space, chassis and suspension, safety features, quantity and brand of audio speakers, music membership service, separation band, screen reflection, CarLife, and map services. Addressing these issues could significantly enhance Hyundai's competitiveness in the Chinese market. Previous studies mainly focused on literature research and surveys, which only revealed consumer perceptions limited to the variables set by the researchers. This study, through text mining and comparing various car brands, aims to gain a deeper understanding of market trends and consumer preferences, providing useful information for marketing strategies of Hyundai and other brands in the Chinese market.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.22-32
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• 2017

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing 'food tourism' keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of 'core service' and 'social marketing' was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; 'socioeconomic circumstance' related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named 'core service', 'social marketing', 'destinations' and 'social environment'. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.

• Child Health Nursing Research
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• v.25 no.4
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• pp.458-467
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• 2019

Purpose: The purpose of this study was to identify human papillomavirus (HPV) vaccination research trends by visualizing a keyword network. Methods: Articles about HPV vaccination were retrieved from the PubMed and Web of Science databases. A total of 1,448 articles published in 2006~2016 were selected. Keywords from the abstracts of these articles were extracted using the text mining program WordStat and standardized for analysis. Sixty-four keywords out of 287 were finally chosen after pruning. Social network analysis using NetMiner was applied to analyze the whole keyword network and the betweenness centrality of the network. Results: According to the results of the social network analysis, the central keywords with high betweenness centrality included "health education", "health personnel", "parents", "uptake", "knowledge", and "health promotion". Conclusion: To increase the uptake of HPV vaccination, health personnel should provide health education and vaccine promotion for parents and adolescents. Using social media, governmental organizations can offer accurate information that is easily accessible. School-based education will also be helpful.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.13-18
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• 2019

In this study, we investigate important keywords and their relationships among the keywords for social issues, and analyze topics to find subjects of the social issues. In particular, we collected twitter data with the keyword 'metoo' which has attracted much attention in these days, and perform keyword analysis and topic modeling. First, we preprocess the twitter data, identified important keywords, and analyzed the relatedness of the keywords. After then, topic modeling is performed to find subjects related to 'metoo'. Our experimental results showed that relatedness of keywords and subjects on social issues in twitter are well identified based on keyword analysis and topic modeling.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.193-201
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• 2018

In this paper, we extracted relevant patent data and conducted statistical analysis to understand the technical development trend related to Wireless Power Transfer (WPT) for Light Rail Transit (LRT). Recently, with the development of WPT technologies, the Light Rail Transit (LRT) industry is concentrating on applying WPT to the power supply system of trains because of their advantages compared wired counterpart, such as low maintenance cost and high stability. This technology is divided into three areas: wireless feeding and collecting technology, high-frequency power converter technology and orbital and infrastructure technology. From each specific area, key words in patent document were extracted by TF-IDF method and analyzed by social network. In the keyword network, core word of each specific technology were extracted according to their degree centrality. Then, the multi-word phrases were also built to represent the concept of core technologies. Finally, based on the analysis results, the development strategies for each specifics technical area of WPT in LRT filed will be provided.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.