• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.463-469
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• 2023

The aim of this study is to provide basic data that can contribute to improving successful clinical adaptation and reducing turnover of new graduate nurses by analyzing research related to reality shock experienced by new graduate nurses using text network analysis. The topics of reality shock experienced by new graduate nurses were extracted from 115 papers published in domestic and foreign journals from January 2002 to December 2021. Articles were retrieved from 6 databases (Korean DB: DBpia, KISS, RISS /International DB: Web of science, Springer, Scopus). Keywords were extracted from the abstract and organized using semantic morphemes. Network analysis and topic modeling for subject knowledge structure analysis were performed using NetMiner 4.5.0 program. The core keywords included 'new graduate nurses', 'reality shock', 'transition', 'student nurse', 'experience', 'practice', 'work environment', 'role', 'care' and 'education'. In recent articles on reality shock experienced by new graduate nurses, three major topics were extracted by LDA (Latent Dirichlet Allocation) techniques: 'turnover', 'work environment', 'experience of transition'. Based on this research, the necessity of interventional research that can effectively reduce the reality shock experienced by new graduate nurses and successfully help clinical adaptation is suggested.

• Knowledge Management Research
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• v.23 no.4
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• pp.69-89
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• 2022

As we enter a super-aged society, senior citizens' health issues are affecting a variety of fields, including medicine, economics, society, and culture. In this study, we intend to draw implications from unstructured data analysis such as text mining and social network analysis in order to apply digital health care service design for improving the cognitive ability of senior citizens. The research procedure of this study improved the service design methodology into a process suited to the analysis of unstructured data, and six steps were applied. Related keywords that exist on social media, focusing on cognitive improvement and healthcare for senior citizens, were collected and analyzed, and based on these results, the direction of healthcare service design for improving on the cognitive abilities of senior citizens was derived. The results of this study are expected to have academic and practical implications for expanding the scope of the use of big data analysis methods and improving existing healthcare service development methodologies.

• Korean Journal of Cognitive Science
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• v.17 no.2
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• pp.103-118
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• 2006

The main purpose of this paper is to investigate the effects of discourse topic and morpho-syntactic verbal information on the resolution of null pronouns in the Korean narrative text within the framework of the construction-integration theory (Kintsch, 1988, Singer & Kintsch, 2001, Graesser, Gernsbacher, & Goldman. 2003). For the purpose of this paper, two conditions were designed: an explicit condition with both a consistently maintained discourse topic and the person-specific verb modals on one hand, and a neutral condition with no discourse topic or morpho-syntactic information provided, on the other. We measured the reading tines far the target sentence containing a null pronoun and the question response times for finding an antecedent, and the accuracy rates for finding an antecedent. During the experiments each passage was presented at a tine on a computer-controlled display. Each new sentence was presented on the screen at the moment the participant pressed the button on the computer keyboard. Main findings indicate that processing is facilitated by macro-structure (topicality) in conjunction with micro-structure (morpho-syntax) in pronoun interpretation. It is speculated that global processing alone may not be able to determine which potential antecedent is to be focused unless aided by lexical information. It is argued that the results largely support the resonance-based model, but not the minimalist hypothesis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.609-614
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• 2018

This study was conducted to investigate the effects of turmeric powder on jeung-pyun. Turmeric jeung-pyun containing 0%, 0.5%, 1%, 1.5%, and 2% turmeric powder was prepared and the moisture, pH, sugar, color, texture, DPPH and sensory properties of the samples were measured. Moisture contents of jeung-pyun were 51.26~51.99% and there were significant differences among the samples(p<0.001). The L-values were significantly decreased with increasing turmeric powder content. The b-value was low in the control and there were significant differences among the samples(p<0.05). Texture profile analysis showed that there were no significant differences among the groups in hardness, adhesiveness, springiness, cohesiveness, gumminess, and chewiness. The hardness was the lowest in the control group and increased with increasing turmeric powder content. The antioxidant activities as measured by DPPH increased with increasing turmeric powder content (p<0.001). In the sensory evaluation, 1% addition of turmeric powder showed the highest preference in terms of color, taste, flavor, texture and overall preference(p<0.001). As determined by this study, the addition of 1% turmeric powder was the most favorable method for making use of turmeric powder in the production of jueng-pyun.

• Information Systems Review
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• v.18 no.1
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• pp.141-155
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• 2016

This study identified terminologies related to the proximity and frequency of disaster by social network analysis (SNA) and text mining, and then expressed the outcome into a mind map. The termdocument matrix of text mining was utilized for the terminology proximity analysis, and the SNA closeness centrality was calculated to organically express the relationship of the terminologies through a mind map. By analyzing terminology proximity and selecting disaster response-related terminologies, this study identified the closest field among all the disaster response fields to disaster response and the core terms in each disaster response field. This disaster response terminology analysis could be utilized in future core term-based terminology standardization, disaster-related knowledge accumulation and research, and application of various response scenario compositions, among others.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.277-284
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• 2020

Text mining is a representative method of big data analysis that extracts meaningful information from unstructured and large amounts of text data. Social media such as Twitter generates hundreds of thousands of data per second and acts as a one-person media that instantly and directly expresses public opinions and ideas. The traditional media are delivering informations, criticizing society, and forming public opinions. For this, we compare the responses of SNS with the responses of media on the issue of the termination of the Korea-Japan GSOMIA (General Security of Military Information Agreement), one of the domestic issues in the second half of 2019. Data collected from 201,728 tweets and 20,698 newspaper articles were analyzed by sentiment analysis, association keyword analysis, and cluster analysis. As a result, SNS tends to respond positively to this issue, and the media tends to react negatively. In association keyword analysis, SNS shows positive views on domestic issues such as "destruction, decision, we," while the media shows negative views on external issues such as "disappointment, regret, concern". SNS is faster and more powerful than media when studying or creating social trends and opinions, rather than the function of information delivery. This can complement the role of the media that reflects public perception.

• Informatization Policy
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• v.28 no.3
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• pp.73-94
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• 2021

This study aimed to analyze the minutes of the Legislation and Judiciary Committee, one of the standing committees of the Korean National Assembly, by applying the WORDFISH algorithm of automated text analysis to estimate the pattern of changes in the ideological orientation of the members of Korea's political elite. The results of the analysis showed that the Legislation and Judiciary Committee generally undergoes changes in ideological orientation around the time of a major administrative change, especially during the period preceding a change up to the time of its implementation. Compared with the United States, where changes in the ideological orientation of the political elite occur simultaneously based on parties, changes in that of the political elite at the Korean National Assembly tend to occur in response to a certain transitional point in time or a change in the ruling government. What is especially noteworthy in terms of the ideological orientation reflected in the minutes of the Legislative Judiciary Committee is that the microscopic effect tends to disappear when the macroscopic effect occurs and, conversely, that the microscopic effect emerges once the macroscopic effect has disappeared. In other words, changes in the ideological orientation of the political elite appear to indicate the effect of a particular legislator's individual characteristics when no effect is observed during a given term or year of the National Assembly, whereas they revealed the effect of a given time itself when no effects related with the individual characteristics of a legislator are discerned.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.51-63
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• 2021

Identifying characters who appear in a story and analyzing their relationships is essential to understanding the story. This paper aims to identify the two actants (or character roles) as Helper and Opponent in Greimas's Actantial model by identifying Subject (i.e., protagonist) and analyzing the emotional interactions between the Subject and the two actants (Helper/Opponent). Our proposed method consists of three steps. First, we identify objects (i.e., characters) appearing in the text story. Next, we extract relational information through the interaction of the characters, and then classify emotions in the text expressed as relational information. Finally, we represent the flow of emotional relations among characters as a directed graph. The node with the highest degree is considered as the Subject because it includes the most relational information. The node that sends the most positive/negative emotions to the Subject is considered as the Helper/Oppenent, respectively. Our research contributes to the computer-based narrative understanding by providing a computational method that automatically extracts the three key character roles (Subject, Helper, and Opponent) from the text story.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.164-172
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• 2018

This paper examines the relevance of pre-production and storytelling in big data analysis and, focusing on JTBC's Man to Man series, looks at how the drama's storytelling should be structured. In this study, we conducted text mining on blogs focused on a particular topic to read the viewer's thoughts on pre-produced dramas and on 67 blogs written about Pre-Production Dramas from 2016.12.15 to 2017.12.15. Also, we conducted sentiment analysis about the Man to Man series, which is not only a pre-production drama, but also has storytelling issues. The blog text extraction and text mining were analyzed using the OutWit Hub and the R, and the tools.provided by social metrics were used to make sentiment analyses of the larger data. Sentiment analysis revealed that the viewers of the Man to Man series did not agree with the romance between Kim Sul-woo and Cha Do-ha, due to the lack of reality in the female characters. Therefore, it was concluded that it is crucial to increase the reality of the characters in order to increase the audience's empathy. These studies will continue to be necessary, because they will form the basis for digitally driven storytelling studies and will provide valuable materials for conducting predictions and instructions in the cultural content industry.