• Journal of Korea Society of Industrial Information Systems
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• v.26 no.6
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• pp.119-133
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• 2021

Research has been conducted in many fields to identify research trends using text mining, but in the field of emotional labor, no research has been conducted using text mining to identify research trends. This study uses text mining to deeply analyze 1,465 papers at the Korea Citation Index (KCI) from 2004 to 2019 containing the subject word 'emotional labor' to understand the trend of emotional labor researches. Topics were extracted by LDA analysis, and IDM analysis was performed to confirm the proportion and similarity of the topics. Through these methods, an integrated analysis of topics was conducted considering the usefulness of topics with high similarity. The research topics are divided into 11 categories in descending order: stress of emotional labor (12.2%), emotional labor and social support (12.0%), customer service workers' emotional labor (10.9%), emotional labor and resilience (10.2%), emotional labor strategy (9.2%), call center counselor's emotional labor (9.1%), results of emotional labor (9.0%), emotional labor and job exhaustion (7.9%), emotional intelligence (7.1%), preliminary care service workers' emotional labor (6.6%), emotional labor and organizational culture (5.9%). Through topic modeling and trend analysis, the research trend of emotional labor and the academic progress are analyzed to present the direction of emotional labor research, and it is expected that a practical strategy for emotional labor can be established.

• Journal of the Korean Society of Floral Art and Design
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• no.41
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• pp.93-114
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• 2019

The purpose of this study is to analyze the characteristics and problems of the content of flower design education videos on YouTube and to search for improvement direction. The subjects of analysis were 129 pieces of videos uploaded in the last one year including 'craftman floral design' as a search term. The result shows that contents covered were practical lectures, theory lectures, test related tips, job and character introduction, test work, educational guidance and publicity. The production format could be divided into studio lecture, classroom lecture, video feature, interview, Vlog, and television program. The hub-type programming strategy that periodically uploads the videos satisfying the target audiences' interests is mostly applied. The type of lecture covered 'practical skill test' got a good response from the users. Overall, content diversity, interaction between creators and users, and harmonious programming strategies are lacking. In order to improve this, it is necessary for emotional and expressive creators to pioneer differentiated fields and practice based on actual field. The introduction of interactive elements such as games and quizzes and the application of new media technologies such as VR and AR are worth trying. Three strategic types of 'hero', 'hub', and 'how to' should be applied complementary. As the demand for education content related to flower design is expected to expand in the future, it is required to develop content that can be used in various platforms, foster professional creators, and develop associated business models.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.556-576
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• 2023

This study attempted to find a way to utilize idle spaces in rural areas as a way to respond to rural extinction. Based on the keywords "startup," "youth start-up," and "youth start-up+rural," start-up+rural," the study sought to identify the perception of idle facilities in rural areas through the keywords "Idle facilities" and "closed schools." The study presented basic data for policy direction and plan search by reviewing frequency analysis, major keyword analysis, network analysis, emotional analysis, and domestic and foreign cases. As a result of the analysis, first, it was found that idle facilities and school closures are acting importantly as factors for regional regeneration. Second, in the case of youth startups in rural areas, it was found that not only education on agriculture but also problems for residence should be solved together. Third, in the case of young people, it was confirmed that it was necessary to establish digital utilization for agriculture by actively starting a business using digital. Finally, in order to attract young people and revitalize the region through best practices at home and abroad, policy measures that can serve as various platforms such as culture and education as well as startups should be presented in connection with local residents. These results are significant in that they presented implications for youth start-ups in rural areas by reviewing start-up recognition for the influx of young people as one of the alternatives for the use of idle facilities and regional regeneration, and if additional solutions are presented through field surveys, they can be used to set policy goals that fit the reality.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of fish pathology
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• v.25 no.3
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• pp.263-270
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• 2012

This study surveyed for the prevalence of parasites, bacteria and viruses in four fish species, olive flounder (Paralichthys olivaceus), red sea bream (Pagrus major), black sea bream (Acathopagrus schlegeli) and black rockfish (Sebastes schlegeli) in 2010. The survey was aimed to compare the pathogens detected from wild and cultured fish for an epidemiological study. Anisakis sp. was predominantly detected from wild olive flounder and red sea bream (58.6% and 41.7% respectively), but not from the cultured fishes, suggesting anisakid infection is rare in cultured fish. The wild fish get in contact with the anisakids through their prey such as small fishes or crustaceans which carry the anisakids; whereas the cultured fish are fed with formulated feed, free of anisakids. Bacterial detection rates from the wild fishes examined in the study were lower than those of cultured fishes. Vibrio sp. dominated among detected bacterial population in cultured olive flounder (18%). Since vibriosis is known as a secondary infection caused by other stressful factors such as parasitic infections, handling and chemical treatment, it seems that cultured olive flounder are exposed to stressful environment. Viruses diagnosed in the study showed difference in distribution between wild and cultured fishes; hirame rhabdovirus (HRV) (0.1%) and lymphocystis disease virus (LCDV) (3.9%) were detected in the cultured olive flounder, but not in the wild fish, and marine birnavirus (MBV) (1.7%) and red sea bream iridovirus (RSIV) (3.2%) were detected from the wild and cultured red sea bream, respectively. From the survey conducted, it can be concluded that even though some pathogens (Trichodina sp., Microcotyle sp., etc.) are detected from both the wild and cultured fish, pathogens such as Anisakis sp., Vibrio sp. and LCDV showed difference in distribution in the wild and cultured host of same fish species and this can be attributed to their environmental condition and feeding.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Korea Entertainment Industry Association
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• v.13 no.7
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• pp.99-118
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• 2019

This study confirmed the followings based on analysis of language messages in 718 advertisement in , AMOREPACIFIC's beauty magazine, published from 1958 to 2018 by product categories, era, in terms of purchase information, persuasive expression, word type. First, the number of pieces among 1980s to 1990s advertisement were the largest and, in terms of product categories, there were the greatest number of pieces in skincare, makeup and mens products. Second, headline and bodycopy had a different aspect in persuasive expression. "focused on image-making" was mainly used for head lines. Specifically, "situational image" was generally dominant. While the "user image" was higher before 1990's, "brand image" was as recent times. "Informal" was mostly applied for bodycopies, especially, "general information" and "differentiated information" was used the most. It is important to know what kind of information the brand established in each brand should be embodied rather than simply dividing the appeal method into "rational appeal" and "emotional appeal."Third, persuasive expression has different aspects in headlines and body copies. "focused on image-making" was mainly used as headlines. Specifically, "situational image" is dominant. Also, "user image" was high before 1990s but "brand image" got higher in recent times. "Informal" was mostly used as body copies, especially "general information" and "differentiated information" were the most frequently selected. Therefore, it is important to apprehend which information to specify established images by brands, rather than to divide "rational appeals" and "emotional appeals". Lastly, categorizing word type into brand names and headlines, foreign language was the most dominant in brand names and Chinese characters in headline. Remarkably, brand names in native language temporarily high in 70's and 80's, which could be interpreted to be resulted from the government policy promoting native language brands in those times. In addition, foreign language was frequently used in cosmetics and Chinese characters in men's product. It could be explained that colors or seasons in cosmetic products were expressed in foreign language in most case. On the other hand, the inclination of men's product consumers, where they pursue prestige or confidence in Chinese character, was actively reflected to language messages.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.3 no.4
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• pp.213-218
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• 1970

Many investigations have been made on V. parahaemolyticus but to the author's knowledge a report on V. parahaemolyticus found in Korean coastal water has not yet been published. The authors have investigated distribution of V. parahaemolyticus in fish, shellfish, mud, crustacea, sea water and cephalopoda in order to determine the possible origins of food poisoning in Korea. The results are summarized as follows: 1. Fifty six of V. parahaemolyticus were isolated from 517 samples obtained from mud, sea water, fish, crustacea and cephalopoda. 2. The distribution of V. parahaemolyticus isolated from the samples was: 6 strains from mud samples, 7 strains from 44 sea water samples, 28 strains from 241 fish samples, 1 strain from 50 crustacea samples and 2 strains from 34 cephalopoda samples. 3. The strains isolated in the Mokpo area were 7 strains from 48 samples and those isolated in the Pohang area were 2 strains from 46 samples. The number of strains in the Mokpo area was the highest among the strains and the number of strains in the Pohang area was the lowest.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.2
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• pp.61-73
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• 2021

This study aimed to identify the landscape perception and landscape characteristics of users by utilizing SNS data generated by their experiences. Therefore, how to recognize the main places and scenery appearing on the island, and what are the characteristics of the main scenery were analyzed using online text data and photo data. Text data are text mining and network structural analysis, while photographic data are landscape identification models and color analysis. As a result of the study, First, as a result of frequency analysis of Bakji·Banwol-do topics, we were able to derive keywords for local landscapes such as 'Purple Bridge', 'Doori Village', and location, behavior, and landscape images by analyzing them simultaneously. Second, the network structure analysis showed that the connection between key and undrawn keywords could be more specifically analyzed, indicating that creating landscapes using colors is affecting regional activation. Third, after analyzing the landscape identification model, it was found that artificial elements would be excluded to create preferred landscapes using the main targets of "Purple Bridge" and "Doori Village", and that it would be effective to set a view point of the sea and sky. Fourth, Bakji·Banwol-do were the first islands to be created under the theme of color, and the colors used in artificial facilities were similar to the surrounding environment, and were harmonized with contrasting lighting and saturation values. This study used online data uploaded directly by visitors in the landscape field to identify users' perceptions and objects of the landscape. Furthermore, the use of both text and photographic data to identify landscape recognition and characteristics is significant in that they can specifically identify which landscape and resources they prefer and perceive. In addition, the use of quantitative big data analysis and qualitative landscape identification models in identifying visitors' perceptions of local landscapes will help them understand the landscape more specifically through discussions based on results.