• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.11
• /
• pp.521-526
• /
• 2017

Data format of Big data is diverse and vast, and its generation speed is very fast, requiring new management and analysis methods, not traditional data processing methods. Textual mining techniques can be used to extract useful information from unstructured text written in human language in online documents on social networks. Identifying trends in the message of politics, economy, and culture left behind in social media is a factor in understanding what topics they are interested in. In this study, text mining was performed on online news related to a given keyword using topic - oriented analysis technique. We use Latent Dirichiet Allocation (LDA) to extract information from web documents and analyze which subjects are interested in a given keyword, and which topics are related to which core values are related.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.275-277
• /
• 2019

The purpose of this study is to classify the topics related to the suicide papers published so far and to identify the proporations of the main topics and the trends of the topics over the past 20 years. For this purpose, a text mining technique used in big data analysis was used as a data base of the Korean Journal of Citation Index (KCI), where information sharing about the papers is most active. This study, which grasps the trends of suicide related research according to the changes of the times, will become a basic data for establishing a strategy to adapt the academic direction related to suicide in the future.

• The Journal of Information Systems
• /
• v.29 no.4
• /
• pp.137-151
• /
• 2020

Purpose Domestic asset management fintech companies are expected to grow by leaps and bounds along with the implementation of the "Data bills." Contrary to the market fever, however, academic research is insufficient. Therefore, we want to analyze user reviews of asset management fintech companies that are expected to grow significantly in the future to derive strengths and complementary points of services that have been provided, and analyze key elements of asset management fintech companies. Design/methodology/approach To analyze large amounts of review text data, this study applied text mining techniques. Bank Salad and Toss, domestic asset management application services, were selected for the study. To get the data, app reviews were crawled in the online app store and preprocessed using natural language processing techniques. Topic Modeling and Aspect-Sentiment Analysis were used as analysis methods. Findings According to the analysis results, this study was able to derive the elements that asset management fintech companies should have. As a result of Topic Modeling, 7 topics were derived from Bank Salad and Toss respectively. As a result, topics related to function and usage and topics on stability and marketing were extracted. Sentiment Analysis showed that users responded positively to function-related topics, but negatively to usage-related topics and stability topics. Through this, we were able to extract the key elements needed for asset management fintech companies.

• Research in Community and Public Health Nursing
• /
• v.32 no.4
• /
• pp.467-476
• /
• 2021

Purpose: The purpose of this study is to understand the social perceptions of nurses in the context of the COVID-19 outbreak through analysis of media articles. Methods: Among the media articles reported from January 1st to September 30th, 2020, those containing the keywords '[corona or Wuhan pneumonia or covid] and [nurse or nursing]' are extracted. After the selection process, the text mining and topic modeling are performed on 454 media articles using textom version 4.5. Results: Frequency Top 30 keywords include 'Nurse', 'Corona', 'Isolation', 'Support', 'Shortage', 'Protective Clothing', and so on. Keywords that ranked high in Term Frequency-Inverse Document Frequency (TF-IDF) values are 'Daegu', 'President', 'Gwangju', 'manpower', and so on. As a result of the topic analysis, 10 topics are derived, such as 'Local infection', 'Dispatch of personnel', 'Message for thanks', and 'Delivery of one's heart'. Conclusion: Nurses are both the contributors and victims of COVID-19 prevention. The government and the nurses' community should make efforts to improve poor working conditions and manpower shortages.

• Journal of Information Technology Services
• /
• v.22 no.2
• /
• pp.93-127
• /
• 2023

This study analyzed the CEO New Year's addresses of major Korean companies, extracting key topics for employees via text mining techniques. An intended contribution of this study is to assist reporters, analysts, and researchers in gaining a better understanding of the New Year's addresses by elucidating the implicit and implicative features of messages within. To this end, this study collected and analyzed 545 New Year's addresses published between 2012 and 2021 by the top 66 Korean companies in terms of market capitalization. Research methodologies applied include text clustering, word embedding of keywords, frequency analysis, and topic modeling. Our main findings suggest that the messages in the New Year's addresses were categorized into nine topics-organizational culture, global advancement, substantial management, business reorganization, capacity building, market leadership, management innovation, sustainable management, and technology development. Next, this study further analyzed the managerial significance of each topic and discussed their characteristics from the perspectives of time, industry, and corporate groups. Companies were typically found to emphasize sound management, market leadership, and business reorganization during economic downturns while stressing capacity building and organizational culture during market transition periods. Also, companies belonging to corporate groups tended to emphasize founding philosophy and corporate culture.

• Korea Trade Review
• /
• v.47 no.4
• /
• pp.137-159
• /
• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

• Journal of Information Technology Services
• /
• v.19 no.3
• /
• pp.117-137
• /
• 2020

The development of information technology leads to changes in various industries. In particular, the health care industry is more influenced so that it is focused on. With the widening of the health care market, the market of smart device based personal health care also draws attention. Since a variety of fitness applications for smartphone based exercise were introduced, more interest has been in the health care industry. But although an amount of use of mobile fitness applications increase, it fails to lead to a sustained use. It is necessary to find and understand what matters for mobile fitness application users. Therefore, this study analyze the reviews of mobile fitness application users, to draw key factors, and thereby to propose detailed strategies for promoting mobile fitness applications. We utilize text mining techniques - LDA topic modeling, term frequency analysis, and keyword extraction - to draw and analyze the issues related to mobile fitness applications. In particular, the key factors drawn by text mining techniques are explained through the concept of user experience. This study is academically meaningful in the point that the key factors of mobile fitness applications are drawn by the user experience based text mining techniques, and practically this study proposes detailed strategies for promoting mobile fitness applications in the health care area.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.26 no.2
• /
• pp.87-99
• /
• 2024

Freediving, an underwater leisure sport that involves diving without the use of a breathing apparatus, has gained popularity among younger demographics through the viral spread of images and videos on social media platforms. This study employs prominent Big Data analysis techniques, including text mining, Latent Dirichlet Allocation (LDA) topic analysis, and opinion mining to explore the keywords associated with freediving suits over the past five years. The research aims to analyze the rapidly evolving market trends of freediving suits and the increasingly complex and diverse consumer perceptions to provide foundational data for activating the freediving suit market and developing strategies for sustained growth. The study identified the keyword 'size' related to freediving suits and conducted opinion mining on 'freediving suit sizes'. Although the results showed a higher positive than negative sentiment, negative keywords were also extracted, indicating the need to understand and mitigate the negative factors associated with 'size'. The findings offer vital guidelines for the advancement of the freediving suit market and enhancing consumer satisfaction. This study is important as it contributes foundational data for continuous growth strategies of the freediving suit market.

• The Journal of Information Systems
• /
• v.26 no.4
• /
• pp.327-353
• /
• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• International Journal of Advanced Culture Technology
• /
• v.7 no.4
• /
• pp.295-301
• /
• 2019

Since the third artificial intelligence boom was triggered by deep learning, it has been 10 years. It is time to analyze and discuss the research trends of deep learning for the stable development of AI. In this regard, this study systematically analyzes the trends of research on deep learning over the past 10 years. We collected research literature on deep learning and performed LDA based topic modeling analysis. We analyzed trends by topic over 10 years. We have also identified differences among the major research countries, China, the United States, South Korea, and United Kingdom. The results of this study will provide insights into research direction on deep learning in the future, and provide implications for the stable development strategy of deep learning.