• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Archives of design research
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• v.19 no.5 s.67
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• pp.203-212
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• 2006

With interests and participation in the costumeplay that mimics characters appeared on carton or animation in recent days, the costumeplay becomes one of cultural phenomena. Using a qualitative research method, this study identified costumeplayers' costume consumption pattern and explored its meanings from the perspective of consumption culture. Indeed, this study intended to help for understanding costumeplayer group as a consumer, and to provide basic knowledge about new market analysis related to fashion design and marketing. The results from the analyzing participant observation and in-depth interviews data are as follows: first, costumeplayers usually begin costumeplay by friends' invitations or by themselves and then continue on participating. Through the costumeplay, participants have benefits such as fun, departure from the daily life, and social interaction. Second, participants acquire costumes through purchase, rent, producing or combination of daily wear, but both purchase and rent account high. Third, the meanings of consumption culture in costumeplay include consumption behavior repeating possession and disposal. Also, costumeplayers concerns efficiency when purchasing or renting the costumes, and internet is a place where information search, comparison, and actual purchasing are occurred. Based on the results, fashion design and marketing implication, limitation of this study and further research ideas were suggested.

• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.106-116
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• 2001

Background : Pulmonary thromboembolism is relatively frequent and potentially fatal. However, it is commonly misdiagnosed. The incidence of pulmonary thromboembolism is not decreasing despite advances in diagnosis and effective prophylatic measures. Its potential for significant sequela necessitates a prompt diagnosis and treatment. Unfortunately, there are many difficulties and problems regarding accurate diagnosis. There is a low prevalence of deep vein thrombosis and pulmonary thromboembolism in Korea and only few reports on this subject are available. Method : The clinical features of 36 patients, who were diagnosed with pulmonary thromboembolism at the Korea University medical center, were reviewed. Results : 1) There was no significant difference in prevalence between men an women, and the mean age was 50.9 years in men 59.2 years in women. 2) The frequent causes of pulmonary thromboembolism were malignancies (22.2%), surgery (22.2%), and heart disease(8.2%). Specific causes were not identified in 33.3%. 3) The most common symptom was dyspnea(72.2%), and the most common sign was tachypnea(61.1%). 4) The EKG findings were normal in 28.6%, an S1Q3T3 pulmonale pattern in 25.7%, ST or QRS changes in others. 5) The chest X-ray findings indicated pulmonary infiltration in 37.5%, cardiomegaly in 15.6%, pleural effusion in 12.5%, and normal in 27.8%. The perfusion lung scan showed a high probability in 66.7%, and intermediate or low probability in 33.3%. 6) The pulmonary arterial pressure(PAP) in the high probability groups was 57.9mmHg with a higher mortality rate(35%). Conclusion : Pulmonary thromboembolism is not uncommon in Korea and its clinical features do not differ greatly from thase reported in the literature. When pulmonary thromboemblism of unknown causes are diagnosed, a search for an occult malignancy is recommended. Rapid diagnosis and treatment are achieved when thromboemblism is suspected.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.