• The Journal of Bigdata
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• v.6 no.1
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• pp.51-62
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• 2021

In the field of medical artificial intelligence, there have been a lot of researches on disease prediction and classification algorithms that can help doctors judge, but relatively less interested in artificial intelligence that can help medical consumers acquire and judge information. The fact that more than 150,000 questions have been asked about which hospital to go over the past year in NAVER portal will be a testament to the need to provide medical information suitable for medical consumers. Therefore, in this study, we wanted to establish a classification model that classifies 8 medical subjects for symptom text directly described by patients which was collected from NAVER portal to help consumers choose appropriate medical subjects for their symptoms. In order to ensure the validity of the data involving patients' subject matter, we conducted similarity measurements between objective symptom text (typical symptoms by medical subjects organized by the Seoul Emergency Medical Information Center) and subjective symptoms (NAVER data). Similarity measurements demonstrated that if the two texts were symptoms of the same medical subject, they had relatively higher similarity than symptomatic texts from different medical subjects. Following the above procedure, the classification model was constructed using a ridge regression model for subjective symptom text that obtained validity, resulting in an accuracy of 0.73.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Journal of The Korean Association For Science Education
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• v.27 no.5
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• pp.367-378
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• 2007

The purpose of this study is to investigate the features of science language in Earth science textbooks and Earth science research articles. We examined two Earth science textbooks and two Earth science articles using the taxonomy of scientific words, the text structure analysis of explanations, the analysis of conjunctive relations and reasoning, and the function of conjunction. The results showed that school science language revealed in Earth science textbooks had high proportion of naming words and the text structures in which definition/exemplification structure and description structure were dominant. Also, internal relations that showed additional arrangement rather than logical inference, were predominant in Earth science textbooks. However, scientists' science language revealed in the Earth science articles had more proportion of process words and concept words than the Earth science textbooks and the schematic structure of explanation texts, such as orientation - implication sequence - conclusion. In addition, the text structures in each sentences of implication -sequence showed cause/effect or problem-solving after description structures. Also each sentences expressed causal or abductive reasoning through the internal relations using verbs or adverbial inflection. It is necessary that we bridge the gap between the two languages for students' authentic use of science language. For the bridging, we propose "interlanguage", which mediates between school science language and scientists' language.

• Knowledge Management Research
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• v.25 no.1
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• pp.155-172
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• 2024

This study investigates the effectiveness of foreign language learning through diverse treatments in virtual settings, particularly by differentiating virtual environments with three textual enhancement techniques. A 2 × 3 mixed-factorial design was used, treating virtual environments as within-subject factors and textual enhancement techniques as between-subject factors. Participants experienced two videos, each in different virtual learning environments with one of the random textual enhancement techniques. The results showed that the interaction between different virtual environments and textual enhancement techniques had a statistically significant impact on presence among groups. In examining main effects of virtual environments, significant differences were observed in flow and attitude toward pre-post learning. Also, main effects of textual enhancements notably influenced flow, intention to use, learning satisfaction, and learning confidence. This study highlights the potential of Metaverse in foreign language learning, suggesting that learner experiences and effects vary with different virtual environments.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.103-116
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• 2020

This paper starts with the recognition of the problem of the need for a link between text-centered acting and body-centered acting. This study is focused on Brecht's theory of acting to overcome loss of presence by repetition which have been discussed many times by not only actors, but also acting educators. Brecht's acting theory has already been mentioned by many researchers as an alternative to conventional actor training. However, not many studies have been conducted on practical applicable methods. The purpose of this study is to provide the basis for the actual practice of Brecht acting and possibility that his acting theory can serve as a link between text and body-centered acting theory. As a research method, we first conduct theoretical considerations on the concepts and limitations of text-centered representational acting and body-centered post-drama acting. Then distinguish between text and body-centered acting tools among Brecht's epic theatre, to summarize the terms and concepts he uses and to identify the existing effects he reaches while acting. Finally, this paper proposes an teaching model that transforms and develops Brecht's acting theory through the writer's teaching experience. However, there are limitations in generalizing its effectiveness because this study is based on the writer's experience. We hope that further research will help the diversity of acting education by developing in-depth insights on Brecht acting theory and various models of acting teaching methods.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.253-274
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• 2020

In this study, text mining and main path analysis (MPA) were applied to understand the origins and development paths of research areas that make up the mainstream of Korean studies. To this end, a quantitative analysis was attempted based on digital texts rather than the traditional humanities research methodology, and the main paths of Korean studies were extracted by collecting documents related to Korean studies including citation information using a citation database, and establishing a direct citation network. As a result of the main path analysis, two main path clusters (Korean ancient agricultural culture (history, culture, archeology) and Korean acquisition of English (linguistics)) were found in the key-route search for the Humanities field of Korean studies. In the field of Korean Studies Humanities and Social Sciences, four main path clusters were discovered: (1) Korea regional/spatial development, (2) Korean economic development (Economic aid/Soft power), (3) Korean industry (Political economics), and (4) population of Korea (Sex selection) & North Korean economy (Poverty, South-South cooperation).

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.282-290
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• 2018

This study tried to suggest the development of a writing education model for Christian Universities based on both Hobbs' media education model and Vanhoozer's media text analysis model. This model consists of 6 steps- Christian worldview establishment, access, analysis and evaluation, creation, reflection and social action. This was developed in order to be applied for the class "reading and writing" of liberal arts. Also, this is an appropriate model for media text writing education aiming at an alternative creation activity through a critical comprehension of the complex texts consisting of sounds, images, letters and so on. Furthermore, this is designed to train the capable persons having intelligence, character, and spirituality balanced, whom the education of Chrisitian universities aims at. Finally, this model pursues the student-friendly and amalgamative text writing appropriate for a new era and has an advantage to raise the power of various forms of letter writing which all the universities should stress as well.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.1-12
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• 2020

The purpose of this study is to explore the trend of blockchain technology through analysis of patents and news articles using text mining, and to suggest the blockchain policy agenda by grasping social interests. For this purpose, 327 blockchain-related patent abstracts in Korea and 5,941 full-text online news articles were collected and preprocessed. 12 patent topics and 19 news topics were extracted with latent dirichlet allocation topic modeling. Analysis of patents showed that topics related to authentication and transaction accounted were largely predominant. Analysis of news articles showed that social interests are mainly concerned with cryptocurrency. Policy agendas were then derived for blockchain development. This study demonstrates the efficient and objective use of an automated technique for the analysis of large text documents. Additionally, specific policy agendas are proposed in this study which can inform future policy-making processes.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.17-27
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• 2020

News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.68-74
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• 2019

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.