• The Journal of the Korea Contents Association
• /
• v.19 no.9
• /
• pp.629-636
• /
• 2019

In order to infer whether the Special Act on the Punishment of Child Abuse Crimes, etc. actually brought about a change in the social perception shared about child abuse in our society, we used big data to examine the change in the perception of child abuse by the public. This study selected 'child abuse' as the keyword and collected and analyzed. The results of this study are as follows. First, before the implementation of the Special Act in 2013, the words "china" are kindergarten, teacher, body, problem, reporting obligation and neglect compared to the following. After the implementation of the special law, daycare centers, incidents, eradication, campaigns, domestic violence and preventive education were newly introduced. Second, the interconnection of key words in the previous picture of 2013 shows that the left group focuses on measures to introduce to prevent child abuse while the right group consists of keyword intended to view child abuse in conjunction with domestic violence. They are still seen as a group of divorces, discipline, neglect and parental education, which they still perceive as a family problem. Since the implementation of the Special Act in 2013, it will be divided into four groups, and the top group will be highlighted by the keyword related to child abuse cases, part of suspected cases and awareness of child abuse. In addition, the Act on the Special Cases of Child Abuse and the Child Protection Agency clearly appear as a child protection system.

• Knowledge Management Research
• /
• v.23 no.3
• /
• pp.129-152
• /
• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.101-123
• /
• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.179-200
• /
• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Cross-Cultural Studies
• /
• v.44
• /
• pp.211-237
• /
• 2016

This paper seeks to explore the new relationship between music and poetry established in the beginning of the Modern Era. This was a period when reading silently was the dominant culture rather than reading aloud and orality was limited due to the emergence of literacy and print culture. A poet sensitive to the characteristics of the period, $Mallarm{\acute{e}}$ created his own concept of music and new creative principles of poetry from it. We analyze his "Divigation" and letters, in particular, the "Crisis of vers", "Music and Literature", "Mystery in the letters", and "About the book." Firstly, $Mallarm{\acute{e}}$ connects music with the mystery and the sacred: the mystery surrounds the music and the music is oriented with the sacred. The sanctity is that of the human race and has existed within humans since the beginning. Transposing the characteristics of this music to the poetry is his first creative principle of poetry. However, $Mallarm{\acute{e}}$ called music a totality of relationships that exist between objects without reducing the dimension to only the instruments or the sound. His definition is abstract, regarding music as a complete rhythm, the atmosphere and the air. Secondly, we have the question of how to realize music in a poem. As the music is surrounded by the mystery, $Mallarm{\acute{e}}$ can transpose the sacred to a poem in mysterious ways. This leads to his second principle of poetry: make a poem as a structure. In other words, 'musically', based on the disappearance of real objects and the initiative of the poet, he created a structure with only the words. We can create an acoustic structure but $Mallarm{\acute{e}}$ created a visible structure to overcome the incompleteness of the sound of a word in the diffusion of print culture. In this manner, the use of silence as much as sound and the use of visual as much as aural components were introduced in poetry as important motifs and the essentials of creation. This new relationship between poetry and music and the creative principles drawn from it appear to be the areas to which attention should be focused in the research of poetry.

• Journal of the Daesoon Academy of Sciences
• /
• v.37
• /
• pp.53-105
• /
• 2021

The purpose of this study is to clarify the meaning of Gwonji (權智, Authority and Foreknowledge) through the phrases contained in the section, Gwonji of the Jeon-gyeong (known in English as The Canonical Scripture), and to compare the changes that each verse from Gwonji underwent by juxtaposing it against the sixth edition of Daesoon Jeong-gyeong (which was published prior to the Jeon-gyeong) to explore the term's literary meaning. In order to save the world, Sangje descended to human world and performed the Cheonjigongsa (Reordering Works of the Universe) for nine years with the power he exercises over the Three Realms of Heaven, Earth, and Humanity. Based on the plan set by the Cheonjigongsa, Sangje's teachings were spread to humanity and provided as the basis for building the earthly paradise. From this perspective, this study demonstrates its significance by providing a comprehensive approach to the Jeon-gyeong by highlighting the subject of Sangje's authority and wisdom as recorded in the section titled Gwonji. There is also value in the variant verses from Gwonji that the study discovered by comparing and analyzing the phrases from chapters one and two of Gwonji as they appear in the Jeon-gyeong with their equivalents from the sixth edition of Daesoon Jeong-gyeong, which was published in 1965, nearly a decade before Daesoon Jinrihoe's publication of the Jeon-gyeong in 1974. The results of this comparative study of parallel passages related to Gwonji are as follows: First, Gwonji can be understood as the authority and wisdom of Sangje, and this is the core element in realizing the Earthly Paradise through His Cheonjigongsa. Second, phrases related to Sangje's authority and wisdom are spread out in the seven sections of the Jeon-gyeong, and they were written to emphasize the main purpose suggested in each section or chapter. Third, in sections other than Gwonji, the great power of Sangje is exercised to treat matters related to deities and social problems, whereas in Gwonji part, it is dedicated to the performance of Cheonjigongsa. Fourth, there are five sections of the Jeon-gyeong which are organized into chapters. All of these sections and their chapters indicate the year when key events transpired. Fifth, when passages from chapter one of Gwonji is compared to parallel passages from Daesoon Jeon-gyeong, there are several verses that vary in terms of their wording and also sentences that indicate a different dates or times for certain events.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.1-21
• /
• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.