• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.53-59
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• 2017

This study was attempted to identify the domestic research related to depression and stress. The subjects of the analysis were 1,875 college degree theses thrown in the National Assembly Library searched by the depression and stress keyword as of November 30, 2016. The analysis method visualizes atypical data with Word Cloud, which is one of the text mining techniques. We also used the R'LDA package and LDA to classify treatment and subjects. As a result of the analysis, 233(12.4%) of the total papers with therapeutic keywords were found. Application of treatment methods was art therapy, music therapy, horticultural therapy, cognitive behavior therapy, clinical art therapy, cognitive therapy, psychological therapy, depression treatment, group therapy, laughter treatment sequence. The study subjects were adolescents, elderly, patient, mother, child, female, parents, and college students in order. The results of LDA topic analysis for adolescents were classified into four topics: self-support, treatment program, relationship effect, and variable study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4191-4211
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• 2019

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.757-763
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• 2019

Youtube has become so popular that it is called the age of YouTube. As the number of users and contents increase, the choice of information increases. However, it is difficult to select information that meets the needs of users. YouTube provides recommendations based on their watch list. Therefore, in this study, we want to analyze the channel of user's subject in various angles and provide the proposed scheme based on the crawled channels, measurement of the perception of channels and channel videos through quantitative data and hidden qualitative data analysis. Based on the above two data analysis, it is possible to know the recognition of the channel and the recognition of the channel video, thereby providing a ranking of the channels that deal with the topic. Finally, as a case study, we recommend English learning channels to users based on numerical data statistics and emotional analysis results to maximize flipped learning effect regardless of time and space.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• The Journal of Information Systems
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• v.27 no.4
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• pp.71-96
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• 2018

Purpose Recently, social interest in the convergence research is at its peak. However, contrary to the keen interest in convergence research, an infrastructure that makes it easier to recruit researchers from other fields is not yet well established, which is why researchers are having considerable difficulty in carrying out real convergence research. In this study, we implemented a researcher recommendation system that helps researchers who want to collaborate easily recruit researchers from other fields, and we expect it to serve as a springboard for growth in the convergence research field. Design/methodology/approach In this study, we implemented a system that recommends proper researchers when users enter keyword in the field of research that they want to collaborate using word embedding techniques, word2vec. In addition, we also implemented function of keyword suggestions by using keywords drawn from LDA Topicmodeling Algorithm. Finally, the UI of the researcher recommendation system was completed by utilizing the collaborative messenger Slack to facilitate immediate exchange of information with the recommended researchers and to accommodate various applications for collaboration. Findings In this study, we validated the completed researcher recommendation system by ensuring that the list of researchers recommended by entering a specific keyword is accurate and that words learned as a similar word with a particular researcher match the researcher's field of research. The results showed 85.89% accuracy in the former, and in the latter case, mostly, the words drawn as similar words were found to match the researcher's field of research, leading to excellent performance of the researcher recommendation system.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.303-315
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• 2020

Purpose - The purpose of this study is to analyze Korea's trade relations centered on the Pacific Alliance (PA), a major economic integration in Latin America, and identify its problems and suggest measures that can be taken by the government and corporations to reinforce economic cooperation. Design/methodology/approach - To improve the level of contribution of the study, an empirical analysis is necessary. However, due to limited data access, the study will approach the topic of trade relations between Korea and the PA with various statistics and literature. Findings - First, there is an urgent need for changes in import-export goods between Korea and the PA, as trade is focused on specific items. Second, although foreign direct investment from Korea to the PA is centered in manufacturing and mining industries, there should be different investment strategies by countries and industries. Third, it is necessary to reinforce commercial cooperation. Korea currently has Free Trade Agreements with Chile, Peru, and Columbia, but not with Mexico, the largest trading partner among the PA. Therefore, Korea must take active measures to sign an FTA with Mexico, which has been put on hold. Research implications or Originality - Latin America has the most thriving market when it comes to Free Trade Agreements worldwide. With the official establishment of the Pacific Alliance (PA) in 2012, the economic integration of Latin America faced entirely new circumstances. Reinforcing economic cooperation with the PA is extremely important for Korea in terms of entering and dominating the Latin American market. However, there is still a lack of research on the Pacific Alliance, and corporations that aim to enter the Latin American market face difficulties due to lack of information. By investigating the Pacific Alliance and its prospects and analyzing the trade relations with Korea, this study will provide strategic measures for corporations that wish to enter the Latin American market.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.115-127
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• 2008

The Native API is a system call which can only be accessed with the authentication of the administrator. It can be used to detect a variety of malicious codes which can only be executed with the administrator's authority. Therefore, much research is being done on detection methods using the characteristics of the Native API. Most of these researches are being done by using supervised learning methods of machine learning. However, the classification standards of Anti-Virus companies do not reflect the characteristics of the Native API. As a result the population data used in the supervised learning methods are not accurate. Therefore, more research is needed on the topic of classification standards using the Native API for detection. This paper proposes a method for re-grouping malicious codes using fuzzy clustering methods with the Native API standard. The accuracy of the proposed re-grouping method uses machine learning to compare detection rates with previous classifying methods for evaluation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.83-91
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• 2020

This study aims to identify research topics and examine the trend of Covid19-related papers on DBpia. Applying latent Dirichlet allocation (LDA), we have extracted seven research topics, each of which concerns "International Dynamics", "Technology & Security", "Psychological Impact", "Biomedical-Related", "Economic Impact", "Online Education", and "Religion-Related". In addition, we used the multinomial logistic model to examine the trend of research topics. We found that the papers mainly cover topics related to "International Dynamics" and "Biomedical-Related" before June 2020, but the topics have become diverse since then. In particular, topics regarding "Economic Impact", "Online Education" and "Psychological Impact" has drawn increased attention of researchers. The findings would provide a guideline for collaboration in Covid19-related research, and could serve as a reference work for active research.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.381-390
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• 2022

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word 'depress,' using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.11-17
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• 2022

The development of digital technology is changing many things in our daily lives and the marketing environment of companies. Therefore, in this research, we grasp the recent overseas research trends of digital marketing. For that purpose, I would like to utilize SCOPUS, a foreign academic database, to grasp the research trends of digital marketing. As a result of the analysis, it was found that the number of digital marketing papers has been increasing continuously since 2013. In addition, as a result of topic modeling analysis, it was found that the 2nd and 4th topics were similar among the 6 topics in total, and the main topics were digital, marketing, research and so on. The results of this research are significant in that they provided information on digital marketing research trends to researchers and business practitioners. In addition, the results of this study provide practical suggestions for corporate marketers to recognize and leverage the importance of digital marketing.