• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.31-40
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• 2017

This research aims to investigate 'Hallyu' contents consumption tendency of consumers from Korea, Japan, and the United States by analyzing their emotional responses. With the development of social media, research on emotion analysis by reviewing text materials has grown. Whereas environmental variables affect consumer demand towards 'Hallyu' contents, little comparative analyses have been conducted on the emotional responses of consumers from different countries. In this research, the emotional prototype model proposed by Russell(1980) used to extract and distinguish emotional words to clarify how people in the three countries differently perceive the Korean drama "Goblin". First of all, the SNS reviews were collected during a two-month period (February 12 to April 12). Second, significant factors were identified in the collected data according to Russell's emotion model. Third, random forest was applied to organize the selected variables in the order of variable importance. Fourth, the correlations among the emotional words were compared. Lastly, the accuracy of the trained model was measured using the test dataset. The results show that "Happy" was found to be the greatest factor in Korea and in the United States and "Pleased" in Japan. Emotional words correlations showed that when watching the drama "Goblin", "passive unpleasure" was the main factor associated with individual's interest in Korea whereas "passive pleasure" was associated with individual's interest in Japan and in the United States. Based on the results, this research suggests the possibility of developing evaluation guidelines for emotional responses of different countries towards 'Hallyu' contents.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.85-105
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• 2012

In recent years, IT advancement has brought out the new Internet communication environment such as online social network services, where people are connected in global network without temporal and spatial limitation. The popular use of online social network helps people share their experience and preference for specific products and services, thus holding large potential to significantly affect firms' business performance through Word-of-Mouth (WOM). This study examines the role of online social network in raising WOM effect on the movie industry by comparing with the similar role of Internet portal, another major online communication channel. Analyzing 109 movies and data from both Twitter and Naver movie, we found that significant WOM effect exists simultaneously in both Twitter and Naver movie. However, we also found that different figures of online viral effects exist depending on the popularity of movies. In the hit movie group, before the movie release, the WOM effect occurs only in Twitter while the WOM effect arises in both Twitter and Naver movie at the same time after the movie release. In the less-popular (or niche) movie group, the WOM effect occurs in both Twitter and Naver movie only before the movie release. Our findings not only deepen theoretical insights into different roles of the two online communication channels in provoking the WOM effect on entertainment products but also provide practitioners with incentive to utilize SNS as strategic marketing platform to enhance their brand reputations.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• Journal of Korea Technology Innovation Society
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• v.21 no.3
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• pp.1021-1049
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• 2018

The purpose of this study is to examine whether organizational justice, including procedural justice and distributive justice, improve employees' innovative behavior through work engagement and knowledge sharing. In addition, it was conducted to investigate whether work engagement and knowledge sharing indirectly affect the relationship between organizational justice and innovative behavior. For the hypothesis test of this study, Hayes (2018) PROCESS Macro was used. Result of the analysis shows that procedural justice, work engagement, and knowledge sharing influenced innovative behavior. The constructs influencing work engagement were procedural justice, distributive justice, knowledge sharing. Also, procedural justice and work engagement were constructs that affected knowledge sharing. In the relationship between procedural justice and innovative behavior, the indirect effect was confirmed in all paths. In the relationship between the distributive justice and the innovative behavior, It was confirmed that there is not the indirect effect only in the path via knowledge sharing. he indirect effect was confirmed in all paths that did not acquire knowledge sharing. In addition, through the PROCESS Macro analysis, we examined the magnitude of the indirect effect of various paths between mediators. The results show that organizational justice can have the greatest effect on innovative behavior through work engagement. The weakness of respondents control by SNS survey is the major limitation of this study. In the future, Further research is needed depending on the nature of the organization, such as the analysis of differences between various industries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.350-361
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• 2019

The fourth industrial revolution based on the intelligent revolution has revolutionized the society as a whole, and it has also affected the defense sector. Various aspects of the war have been changing with the development of technology. In particular, various strategies such as research and development of core technology related to defense unmanned system field and infrastructure are being established based on the fourth industrial revolution technology. In this paper, we have conducted a study to select the technology required for maritime unmanned systems, which can be considered as a priority consideration for the future development of the core technology to be secured prior to the development of the weapon system. First, the core technology prioritization model for the marine unmanned system was established, and the technology fields of the unmanned robot were reclassified and integrated in the related literature such as the classification of the defense technology standard. For the empirical analysis, a questionnaire survey was conducted for 12 specialists who are engaged in the planning of weapons systems, and the importance of technical fields that require development in the development of marine unmanned systems was analyzed. As a result, it was possible to identify the key technology areas that should be considered in selecting the key technologies proposed by the military groups, research institutes, and companies. This could contribute to the establishment of the technology roadmap to develop the marine unmanned system from the future point of view.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.603-612
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• 2021

The purpose of this study is to allow beginner level English learners to experience the English speaking task using pictures, and to analyze the meanings of the experience using a phenomenological research method. As research participants, 10 freshmen majoring in Power Generation Facilities at Korean Polytechnic University in Gangwon-do were selected. Face-to-face interviews and SNS were used for data collection, and Colaizzi's research method was adopted for data analysis. As a result of the analysis, 9 themes, 4 theme clusters, and 2 categories were derived. The results are as follows. First, the participants were able to find hope that they could speak English at their own level through the English speaking task using pictures. Second, they stated that the effect of the visual medium of painting increased concentration and curiosity and lowered anxiety. Third, it was recognized that self-confidence, a speaker like a native speaker, and quickness of speaking improved due to familiarity with speaking English. Fourth, the biggest difficulty in the English speaking task was vocabulary. So, they felt the limitation in explaining the picture, and they were having a lot of trouble in translating Korean words into English words. Finally, through the results of this study, the effect of the medium of picture was confirmed, and necessary future studies were suggested.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.4
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• pp.16-30
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• 2023

As the concept of metaverse has received great attention, interest in metaverse related to landscape architecture is also increasing. The aim of this research is to understand the potential and tasks of applying metaverse in the field of landscape architecture by analyzing the user experience of a metaverse platform. The object of the research is Meta-Everland built in the Roblox platform, which has the most users among landscape architectural metaverses in Korea. NPS of 30 users who have been to Everalnd was investigated after using Meta-Everland with interviews. NPS before the metaverse experience was -16 and NPS after the experience was -24. This result means that the promotion level was lowered after the experience of the metaverse. There were three causes of lowered NPS: lack of users, low-quality graphics and interface, and lack of content. The factor of lack of users was the result of the other two problems. The factor of low technical quality is hard to be improved in a short period of time. Therefore, the main task to improve the metaverse is developing better metaverse content related to landscape architecture. It is more appropriate to develop metaverse-specific content rather than improve reality issues. Applying AR and VR devices, enhancing communication function, and developing potential as a simulation device are needed to be considered.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.7-18
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• 2017

Recently, the number of SNS user has rapidly increased due to smart device industry development and also the amount of generated data is exponentially increasing. In the twitter, Text data generated by user is a key issue to research because it involves events, accidents, reputations of products, and brand images. Twitter has become a channel for users to receive and exchange information. An important characteristic of Twitter is its realtime. Earthquakes, floods and suicides event among the various events should be analyzed rapidly for immediately applying to events. It is necessary to collect tweets related to the event in order to analyze the events. But it is difficult to find all tweets related to the event using normal keywords. In order to solve such a mentioned above, this paper proposes A Generation and Matching Method of Normal-Transient Dictionary for realtime topic detection. Normal dictionaries consist of general keywords(event: suicide-death-loop, death, die, hang oneself, etc) related to events. Whereas transient dictionaries consist of transient keywords(event: suicide-names and information of celebrities, information of social issues) related to events. Experimental results show that matching method using two dictionary finds more tweets related to the event than a simple keyword search.

• Design Convergence Study
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• v.15 no.4
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• pp.45-58
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• 2016

The purpose of this study is to investigate the most effective emoticon type in on-line communication context through analysis decoding(by their interpretation, empathy, reaction) of receiver about emotional message included the various emoticon types. Message types were all 5 - only text message and messages included texticon, graphicon, anicon, and photocon that reflected the transitional process of emoticon. Survey questionnaire that included various emotional situations was developed and utilized to undergraduate students to analyze the differences in their gender and majors. Results are as follow. First, the graphicon, anicon and photocon messages had higher effectiveness than others in the pleasure while the text only message had the highest effectiveness of them in the displeasure. Second, female students responded that the graphicon, anicon and photocon messages were more effective while male students responded that text only message was. Third, between Arts/Physical and Science/Engineering majors had significant differences in some message types, and especially Science/Engineering majors showed higher average than other majors in all of the emoticon types. These results can provide the information to design messages by the emotional situation of sender and gender and major of receiver.