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

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.4
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• pp.24-37
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• 2021

This study was conducted to newly examine the original landscape of Buyongjeong(芙蓉亭) and Juhamnu(宙合樓) areas in Changdeokgung Palace(昌德宮), focusing on the modern period including the Korean Empire, and to derive useful research results for restoration and maintenance in the future. The study results can be summarized as follows. First, the artificial island in Buyongji(芙蓉池) was originally made up of a straight layer using well-trimmed processed stone. However, during the maintenance work in the 1960s and 1970s, the artificial island in Buyongji was transformed into a mixture of natural and processed stones. The handrail installed on the upper part of the artificial island in Buyongji is a unique facility that is hard to find similar cases. The handrail existed even during the Korean Empire, but was completely destroyed during the Japanese colonial period. Second, Chwibyeong(翠屛), which is currently located on the left and right of Eosumun(魚水門), is the result of a reproduction based on Northern bamboo in 2008. Although there is a view that sees the plant material of Eosumun Chwibyeong as Rigid-branch yew, the specific species is still vague. Looking at the related data and circumstances from various angles, at least in the modern era, it is highly probable that the Eosumun Chwibyeong was made of Chinese juniper like Donggwanwangmyo Shrine(東關王廟) and Guncheongung(乾淸宮) in Gyeongbokgung Palace(景福宮). Third, the backyard of Juhamnu was a space with no dense trees on top of a stone staircase-shaped structure. The stone stairway in the backyard of Juhamnu was maintained in a relatively open form, and it also functioned as a space to pass through the surrounding buildings. However, as large-scale planting work was carried out in the late 1980s, the backyard of Juhamnu was maintained in the same shape as a Terraced Flower Bed, and it was transformed into a closed space where many flowering plants were planted. Fourth, Yeonghwadang Namhaenggak(暎花堂 南行閣), which had a library function like Gyujanggak(奎章閣) and Gaeyuwa(皆有窩), was destroyed in the late 1900s and was difficult to understand in its original form. Based on modern photographs and sketch materials, this study confirmed the arrangement axis of Yeonghwadang Namhaenggak, and confirmed the shape and design features of the building. In addition, an estimated restoration map referring to 「Donggwoldo(東闕圖)」 and 「Donggwoldohyung(東闕圓形)」 was presented for the construction of basic data.

• Molecules and Cells
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• v.26 no.5
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• pp.443-453
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• 2008

The Bric-a-brac, Tramtrack, Broad-complex (BTB) domain is a protein-protein interaction domain that is found in many zinc finger transcription factors. BTB containing proteins play important roles in a variety of cellular functions including regulation of transcription, regulation of the cytoskeleton, protein ubiquitination, angiogenesis, and apoptosis. Here, we report the cloning and characterization of a novel human gene, KLHL31, from a human embryonic heart cDNA library. The cDNA of KLHL31 is 5743 bp long, encoding a protein product of 634 amino acids containing a BTB domain. The protein is highly conserved across different species. Western blot analysis indicates that the KLHL31 protein is abundantly expressed in both embryonic skeletal and heart tissue. In COS-7 cells, KLHL31 proteins are localized to both the nucleus and the cytoplasm. In primary cultures of nascent mouse cardiomyocytes, the majority of endogenous KLHL31 proteins are localized to the cytoplasm. KLHL31 acts as a transcription repressor when fused to GAL4 DNA-binding domain and deletion analysis indicates that the BTB domain is the main region responsible for this repression. Overexpression of KLHL31 in COS-7 cells inhibits the transcriptional activities of both the TPA-response element (TRE) and serum response element (SRE). KLHL31 also significantly reduces JNK activation leading to decreased phosphorylation and protein levels of the JNK target c-Jun in both COS-7 and Hela cells. These results suggest that KLHL31 protein may act as a new transcriptional repressor in MAPK/JNK signaling pathway to regulate cellular functions.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.109-121
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• 2021

As the global COVID-19 pandemic is prolonged, the Corona Blue phenomenon, combined with COVID-19 and blue, is intensifying. The purpose of this study is to analyze the current trend of Corona Blue in consideration of the possibility of increasing mental illness and the need for countermeasures, especially after COVID-19. This study tried to find out the relationship between stress and leisure activities before and after COVID-19 by using Corona Blue news article analysis through the topic modeling method, and questionnaire find out the help of stress and leisure activities. This study was compared and analyzed using two research methods. First, a total of 363 news articles were analyzed through topic modeling based on newspaper articles from January 2020, when COVID- 19 was upgraded to the "border" stage, until September, where the social distancing stage was strengthened to stage 2.5 in Korea. As a result of the study, a total of 28 topics were extracted, and similar topics were grouped into 7 groups: mental-demic, generational spread, causes of depression acceleration, increased fatigue, attitude to coping with long-term wars, changes in consumption, and efforts to overcome depression. Second, the SPSS statistical program was used to analyze the level of stress change according to leisure activities before/after COVID-19 and the main help according to leisure activities. As a result of the study, it was confirmed that the average difference in stress reduction according to participation in leisure activities before COVID-19 was larger than after COVID-19. Also, leisure activities were found to be effective in stress relief even after COVID-19. In addition, if the main help from leisure activities before COVID-19 was the meaning of relaxation and recharging through physical and social activities. After COVID-19, psychological roles such as mood swings through nature, outdoor activities, or intellectual activities were found to play a large part. As such, in this study, it was confirmed that understanding the current status of Corona Blue and coping with leisure in extreme stress situations has a positive effect. It is expected that this research can serve as a basis for preparing realistic and desirable leisure policies and countermeasures to overcome Corona Blue.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.30 no.6
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• pp.465-473
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• 2004

Purpose : The essential triad for nerve regeneration is nerve conduit, supporting cell and neurotrophic factor. In order to improve the peripheral nerve regeneration, we used polyglycolic acid(PGA) tube and brain-derived neurotrophic factor(BDNF) gene transfected Schwann cells in sciatic nerve defects of SD rat. Materials and methods : Nerve conduits were made with PGA sheet and outer surface was coated with poly(lactic-co-glycolic acid) for mechanical strength and control the resorption rate. The diameter of conduit was 1.8mm and the length was 17mm Schwann cells were harvested from dorsal root ganglion(DRG) of SD rat aged 1 day. Schwann cells were cultured on the PGA sheet to test the biocompatibility adhesion of Schwann cell. Human BDNF gene was obtained from cDNA library and amplified using PCR. BDNF gene was inserted into E1 deleted region of adenovirus shuttle vector, pAACCMVpARS. BDNF-adenovirus was multiplied in 293 cells and purified. The BDNF-Adenovirus was then infected to the cultured Schwann cells. Left sciatic nerve of SD rat (250g weighing) was exposed and 14mm defects were made. After bridging the defect with PGA conduit, culture medium(MEM), Schwann cells or BDNF-Adenovirus infected Schwann cells were injected into the lumen of conduit, respectively. 12 weeks after operation, gait analysis for sciatic function index, electrophysiology and histomorphometry was performed. Results : Cultured Schwann cells were well adhered to PGA sheet. Sciatic index of BDNF transfected group was $-53.66{\pm}13.43$ which was the best among three groups. The threshold of compound action potential was between 800 to $1000{\mu}A$ in experimental groups which is about 10 times higher than normal sciatic nerve. Conduction velocity and peak voltage of action potential of BDNF group was the highest among experimental groups. The myelin thickness and axonal density of BDNF group was significantly greater than the other groups. Conclusion : BDNF gene transfected Schwann cells could regenerate the sciatic nerve gap(14mm) of rat successfully.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.