• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.189-198
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• 2019

This study aims to make meaning of the reactions of the Korean people through the text mining of comments on videos of the December 2018 MMA performance of intro on YouTube. For this, comments on 15 YouTube videos were collected over the past 10 months. With the collected data, a total of 5,135 comments were analyzed through crawling using the Python and BeautifulSoup programs, data was refined over a total of 3 sessions, and a final total of 5,080 comments were used as analysis material. A mining technique was used for data analysis and the process of refinement, analysis, and visualization was achieved using the Textom program. Research results showed that keyword analysis showed the keywords of 'performance', 'Korea', 'video', 'top', 'cool', 'dance', 'idol', 'legend', 'love', and 'gratitude' in that order and keywords such as 'patriotism' and 'Olympics' also appeared frequently. N-gram analysis showed that comments with contexts such as 'a top performance that will remain a legend among Korean idol performances', and 'an idol performance that displayed the traditional culture of Korea' were in higher ranks. Based on such keyword analysis results, topic modeling was applied and 5 top keywords were extracted from a total of 5 topics. Analysis results of topic contents and distribution showed that topics in the comments of this performance's videos largely consisted of the 3 reactions of 'high praise regarding the stage performance', 'affection towards the fusion and artistic sublimation of Korean traditional dance', and 'gratitude towards the uploading of cool dance videos'

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.705-720
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• 2018

The purpose of this study is to shed light on the meaning and value of interest (in Korean 'Jae-mi') in science education through literature analysis. Literature analyses were conducted on literature related to interest in various fields such as Korean language, psychology, philosophy, and education. Specifically, this study discussed the meaning of interest, the characteristics of the context of experiencing interest, the educational value of interest in science education, and the direction of science education to realize the value of interest. First, it was found that interest is an experience of emotional activation that can be felt through interaction with a specific object, and it is an emotional experience caused by the complex combination of various psychological factors, which is oriented sense, relationship, self, and object. Second, to understand the context of experience of interest, we conducted a topic modeling analysis with 1173 research articles related to interest. As a result of the analysis, it was confirmed that the context of interest is closely related with playfulness. And we addressed that this kind of playfulness is also found in science. Third, the educational values of interest in science education were discussed. In science education, fun is not only an instrumental value to induce science learning behavior, it is also one of the universal experiences that learners feel lively in science teaching-learning, and driving force of individual students' emotional development related to science. The students' active attitude to feel interest lead to creative thinking and action. Finally, we argued that the interest that should be aimed in science education should be active interest and experienced at trial and error, not passive interest induced by external stimuli. And science education culture should be encouraged to respect those who enjoy science. In particular, this study discussed the importance of each student's unique interest experience based on the philosophy of philosopher Deleuze (1976).

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.57-79
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• 2022

COVID-19, which started in December 2019, has had a great impact on our lives in general, including politics, economy, society, and culture, and activities in sports and arts have also been significantly reduced. In the case of sports, sports for all fields in which ordinary citizens participate were particularly affected, and cases of infection in places closely related to people's lives, such as gyms, table tennis, and badminton clubs, also amplified the social fear of the spread of COVID-19. Therefore, in this study, we analyzed news articles related to sports for all at the time when COVID-19 was first spread, and investigated what issues were emerging and being discussed in the sports for all field under the COVID-19 situation. Specifically, we collected news articles dealt with sports for all issues under the COVID-19 situation from Korea's leading portal news sites and identified key sports for all issues by performing topic modeling on these articles. Through the analysis, we found meaningful issues such as COVID-19 outbreak in sports facilities and support for sports activities. In addition, through wordcloud analysis of these major issues, we visually understood the issues and identified the changes in these issues over time.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.215-228
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• 2019

The purpose of this study is to analyze the trends of research themes of the Korean dance in recent 20 years using text mining. The study has analyzed 3,047 words in 1,468 academic papers posted in the Research & Information Services Section(RISS). TEXTOM, a big data analysis solution, has been used to refine and analyse data, and the keyword analysis and topic modeling have been adopted during the text-mining process to come up with meaningful results. First, the theme of studies has shifted from the structure of the basic Korean dance moves to the use and transmission of the Korean dance. Second, those who participate in studies of the Korean dance have changed from middle-aged women to elderly women. Third, studies on dance records have been inactivated. Fourth, studies on Choi Seung-hee have consistently been a subject of interest. Fifth, the focus of studies has turned from the Korean creative dance to the Korean traditional dance. Sixth, there are no iconic research themes that would lead the academic trends with no clear boundaries of research themes.

• Journal of KIISE:Software and Applications
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• v.33 no.9
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• pp.787-801
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• 2006

Ontology merging describes the process of integrating two ontologies into a new ontology. How this is done best is a subject of ongoing research in the Semantic Web, Data Integration, Knowledge Management System, and other ontology-related application systems. Earlier research on ontology merging, however, has studied for developing effective ontology matching approaches but missed analyzing and solving methods of problems of merging two ontologies given correspondences between them. In this paper, we propose a specific ontology merging process and a generic operator, T-MERGE, for integrating two source ontologies into a new ontology. Also, we define a taxonomy of merging conflicts which is derived from differing representations between input ontologies and a method for detecting and resolving them. Our T-MERGE operator encapsulates the process of detection and resolution of conflicts and merging two entities based on given correspondences between them. We define a data structure, MergeLog, for logging the execution of T-MERGE operator. MergeLog is used to inform detailed results of execution of merging to users or recover errors. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Naver philosophy dictionary as input ontologies. Our experiments show that the automatic merging module compared with manual merging by a expert has advantages in terms of time and effort.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Management & Information Systems Review
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• v.35 no.1
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• pp.85-116
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• 2016

The present study aims to explore the causes of employment difficulty on the basis of job applicant's interest from P-E (person-environment) fit perspective. Our approach relied on a textual analytic method to reveal insights from their situational interests in a job search during the change of labor market. Thus, to investigate the type of major interests and psychological responses, user-generated texts in a social community were collected for analysis between January 1, 2013 through December 31, 2015 by crawling the online-community in regard to job seeking and sharing information and opinions. The results of topic analysis indicated user's primary interests were divided into four types: perception of vocation expectation, employment pre-preparation behaviors, perception of labor market, and job-seeking stress. Specially, job applicants put mainly concerns of monetary reward and a form of employment, rather than their work values or career exploration, thus youth job applicants expressed their psychological responses using contextualized language (e.g., slang, vulgarisms) for projecting their unstable state under uncertainty in response to environmental changes. Additionally, they have perceived activities in the restricted preparation (e.g., certification, English exam) as determinant factors for success in employment and suffered form job-seeking stress. On the basis of these findings, current unemployment matters are totally attributed to the absence of pursing the value of vocation and job in individuals, organizations, and society. Concretely, job seekers are preoccupied with occupational prestige in social aspect and have undecided vocational value. On the other hand, most companies have no perception of the importance of human resources and have overlooked the needs for proper work environment development in respect of stimulating individual motivation. The attempt in this study to reinterpret the effect of environment as for classifying job applicant's interests in reference to linguistic and psychological theories not only helps conduct a more comprehensive meaning for understanding social matters, but guides new directions for future research on job applicant's psychological factors (e.g., attitudes, motivation) using topic analysis.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.