• The Journal of Korean Philosophical History
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• no.28
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• pp.267-296
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• 2010

There exists various advantages and disadvantages with various thoughts in human beings. Therefore, it causes very complicated conflict by these diversity. It would be impossible if there would not be solved in classes, areas, schools, cultures whether it is simple or complicated. The proposition which totally shows these logic are Cheng-Yi(程頤, 1033-1107)'s principle is one but its manifestations are many(理一分殊) and Yul-gok(栗谷, 1536-1584)'s all penetrating Li and defining Ch'i. The main concern is how to realize concretly the pure goodness in theory of principal and vital force. There are opinion which Li shows it's pure goodness initiatively and it only has to be shown through Ch'i. Toi-gyeo(退溪, 1501-1570)and Woo-dam(愚潭, 1625-1707) thought the subjectivity lies on Li which shows itself even though Li can't shows itself. On the while, Yul-gok thought it realize through Ch'i, and Li has the superintendence which Li shows itself through Ch'i. Regardless with above, Nok-mun(鹿門, 1711-1788) had the point of view which pure goodness come to realize by the coincidence of Li and Ch'i, on the same time, it didn't matter whether the subjectivity lay on Li or Ch'i, Li showed by nature while Ch'i did by vital energy controlled and worked together. While Yul-gok established his theoretical ground of universality in the best pure good and its bases on the realization, Woo-dam put an emphasis on the practice and realization of goodness to endow with its positive meaning. On the contrary of it, Nok-mun emphasised vital enerty, that is, enabled to realize goodness which premised Li and Ch'i are reciprocally related to other in non-segregated condition(理氣不相離). Still however, the most important thing is how the rightness can be concreately explained which human have to practice its pure goodness. It is not restricted on the Sung Confucianism, it is the material question in philosophy.

• Journal of Technology Innovation
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• v.31 no.3
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• pp.39-73
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• 2023

The field of Management of Technology(MOT) emerged in response to the need for research management within U.S. public research institutions during the 1960s. Since its inception, it has proliferated significantly, being practiced in more than 809 institutions globally and over 19 institutions in Korea, encompassing both research and educational endeavors. Particularly noteworthy is the substantial investment of government resources, primarily channeled through the Ministry of Industry since 2007, which has expeditiously established a comprehensive framework for cultivating graduate-level MOT expertise, marked by both quantitative and qualitative advancements. The educational curriculum in the realm of Korean MOT deviates from foreign counterparts through distinctive pathways, exemplified by its emphasis on industry practice-oriented educational programs, standardization and isomorphism across different schools, as well as its interconnectedness with proximate academic disciplines. This research systematically undertakes an analysis of the curriculum in Korean MOT graduate schools, thereby ascertaining its intrinsic identity and distinct attributes. In this endeavor, a comprehensive examination of eleven principal MOT textbooks(three in Korean and eight in English) is conducted to delineate the primary content of the curriculum across seven thematic domains. Moreover, the study deliberates on its differentiation from neighboring academic disciplines and the definitional attributes of MOT. Subsequently, this analysis also encompasses nine Korean MOT graduate programs, projecting the seven thematic domains onto their respective curricula. The findings illuminate that within the context of Korean graduate programs, a substantial proportion of the curriculum, amounting to 62.5%, is dedicated to facets encompassing the operational aspects of technology management within corporate contexts, technology management specific to varying industries and technologies, and collaborative endeavors between academia and industry in the form of projects and seminars. Evidently, the Korean approach to technology management education is notably geared towards the cultivation of adept practitioners capable of executing technology management functions at a mid-tier managerial level, aligned with the exigencies of regional industries. Grounded in the analysis of technology management curricula, this study extrapolates implications for the future trajectory of MOT education in Korea, encompassing a consideration of the stages of industrial development. It underscores the necessity to augment the educational curricula pertaining conceptual foundation of technology and innovation, strategic perspectives of technology and innovation, and the socio-economic context of technology management.

• 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 Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.