A Study on Differences of Contents and Tones of Arguments among Newspapers Using Text Mining Analysis (텍스트 마이닝을 활용한 신문사에 따른 내용 및 논조 차이점 분석)
-
- Journal of Intelligence and Information Systems
- /
- v.18 no.3
- /
- pp.53-77
- /
- 2012
This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na
People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.
It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.
Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point,
In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.
The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.
Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.
In china, six bibliographies of offical historical books are evaluated at the most important things among the systematically-editing bibliographies. These bibliographies would be usful to study the orign of classical sciences and their development, bibliographic research of Chinese classics, bibliographic judgement on genuine books, titles, authors, volumes. They could be refered to research into graving, correcting, and existence of ancient books. therefore, these bibliographies would be applied to estimation the phase of scientific and cultural development. The study of these bibliographies has been not yet made in Korea. This thesis lays its importance on the background of their appearance, their classification norms, organizing system of their catalogue, and comparison between their difference. 1. Editing and compiling of Chilyak (칠약) by Liu Chin (유흠) and official histories played an important role of entering an apperance of historical book's bibliographies. Chilyak has been lost. However, its classification and compiling system of classical books would be traced by Hansoyemunji(한서예문지) of which basic system is similar to Chilyak. It classified books according to their scientific characteristic. If a few books didn't have their own categories, they were combined by the circles parallel to the books' characteristic. With the books classified under the same scientific characteristic, they were again divided into the scientific schools or structures. It also arranged the same kinds of books according to the chronology. The some books wi th duplicate subjects were classified multiplely by their duplicate subject. 2. Ssu-ma Chon's (사마천) The Historical Records (Saki, 사기) and Pan Ku's (반고) The History of the Former Han Dynasty (Hanso, 한서) has also took effects on appearance of historical books' bibliographies. Covering overall history, Saki was structured by the five parts: The basic annals(본기), the chronological tables (표), the documents (서), the hereditary houses (세가), biographies (열전). The basic annals dealt with kings and courts' affairs according to the chronology. The chronological tables was the records of the annals. The documents described overall the social and cultural systems. The hereditary houses recorded courts' meritorious officials and public figures. The biographies showed exemplars of seventy peoples selected by their social status. Pan Ku(반구)'s The History of the Former Han Dynasty(한서) deserved to be called the prototype for the offical histories after Saki's (사기; The Historical Records) apperance. Although it modelled on Saki, it had set up its own cataloguing system. It was organized by four parts; the basic annals (본기), the chronological tables (표), treatises(지), biographies (열전). The documents in the Hanso(한서) was converted into treatises(지). The hereditary houses and biographies were merged. For the first time, the treatise with The Yemunji could operate function for historical bibliographies. 3. There were six historical bibliographies: Hansoyemunji(한서예문지), Susokyongjeokji (수서경적지), Kudangsokyongjeokji(구당서경적지), Shindangsoyemunji (신당서예문지), Songsayemunji (송사예문지), Myongsayemunji (명사예문지). 1) Modelling on Liu Chin's Chilyak except Chipryak(집략), Hansoyemunji divided the characteristic of the books and documents into six parts: Yukrye(육예), Cheja(제자), Shibu(시부), Pyongsoh(병서), Susul(수술), Pangki(방기). Under six parts, there were thirty eight orders in Hansoyemunji. To its own classification, Hansoyemunji applied the Chilyak's theory of classification that the books or documents were managed according to characteristic of sciences, the difference of schools, the organization of sentences. However the overlapped subjects were deleted and unified into one. The books included into an unsuitable subject were corrected and converted into another. The Hansoyemunji consisted of main preface (Taesoh 대서), minor preface (Sosoh 소서) , the general preface (Chongso 총서). It also recorded the introduction of books and documents, the origin of sciences, the outline of subjects, and the establishment of orders. The books classified by the subject had title, author, and volumes. They were rearranged by titles and the chronological publication year. Sometimes author was the first access point to catalogue the books. If it was necessary for the books to take footnotes, detail notes were formed. The Volume number written consecutively to order and subject could clarify the quantity of books. 2) Refering to Classfication System by Seven Norms (칠분법) and Classification System by Four Norms(사분법), Susokyongjeokji(수서경적지) had accomplished the classification by four norms. In fact, its classification largely imitated Wanhyosoh(완효서)'s Chilrok(칠록), Susokyongjeokji's system of classification consisted of four parts-Kyung(경), Sa(사), Cha(자), Chip(칩). The four parts were divided into 40 orders. Its appendix was again divided into two parts, Buddihism and Taiosm. Under the two parts there were fifteen orders. Totally Susokyongjeokji was made of six parts and fifty five orders. In comparison with Hansoyemunji(한서예문지), it clearly showed the conception of Kyung, Sa, Cha, Chip. Especially it deserved to be paid attention that Hansoyemunji laied history off Chunchu(춘추) and removed history to Sabu(사부). However Chabu(사부) put many contrary subjects such as Cheja(제자), Kiye(기예), Sulsu(술수), Sosol(소설) into the same boundary, which committed errors insufficient theoretical basis. Anothor demerit of Susokyongjeokji was that it dealt with Taiosm scriptures and Buddism scriptures at the appendix because they were considered as quasi-religion. Its compilation of bibliographical facts consisted of main preface(Taesoh 대서), minor preface(Sosoh 소서), general preface (Chongsoh 총서), postscript (Husoh 후서). Its bibliological facts mainly focused on the titles. Its recorded authors' birth date and their position. It wrote the lost and existence of books consecutive to total number of books, which revealed total of the lost books in Su Dynasty. 3) Modelling on the basis of Kokumsorok(고분서록) and Naewaekyongrok(내외경록), Kudangsokyongjeokji(구당서경적지) had four parts and fourty five orders. It was estimated as the important role of establishing basic frame of classification by four norms in classification theory's history. However it had also its own limit. Editing and compling orders of Kudangsokyongjeokji had been not progressively changed. Its orders imitated by and large Susokyongjeokji. In Its system of organizing catalogue, with its minor preface and general preface deleting, Kudangsokyongjeokji by titles after orders sometimes broke out confusion because of unclear boundaries between orders. 4) Shindangsoyemunji(신당서예문지), adding 28,469 books to Kudangsokyongjeokji, recorded 82,384 books which were divided by four parts and fourty four orders. In comparison with Kudangkyongjeokj, Sindangsoyemunji corrected unclear order's norm. It merged the analogical norms four orders (for instance, Kohun 고훈 and Sohakryu 소학류) and seperated the different norms four orders (for example, Hyokyong 효경 and Noneuhryu 논어류, Chamwi 참위 and Kyonghaeryu 경해류, Pyonryon 편년 and Wisaryu 위사류). Recording kings' behaviors and speeches (Kikochuryu 기거주류) in the historical parts induced the concept of specfication category. For the first time, part of Chipbu (집부) set up the order of classification norm for historical and literatural books and documents (Munsaryu 문사류). Its editing and compiling had been more simplified than Kudangsokyongjeokji. Introduction was written at first part of bibliographies. Appendants except bibliographic items such subject, author, title, volume number, total were omitted. 5) Songsayemunji(송사예문지) were edited in the basis of combining Puksong(북송) and Namsong(남송), depending on Sabukuksayemunji(사부국사예문지). Generally Songsayemunji had lost a lot of bibliographical facts of many books. They were duplicated and wrongly classified books because it committed an error of the incorrectly annalistic editing. Particularly Namsong showed more open these defaults. Songsayemunji didin't include the books published since the king Youngchong(영종). Its system of classification was more better controlled. Chamwiryu(참위류) in the part of Kyongbu(경부) was omitted. In the part of history(Sabu 사부), recordings of kings' behaviors and speeches more merged in the annals. Historical abstract documents (Sachoryu 사초류) were seperately arranged. In the part of Chabu(자부), Myongdangkyongmaekryu(명당경맥류) and Euisulryu(의술류) were combined. Ohangryu(오행류) were laied off Shikuryu(시구류). In the part of Chipbu(집부), historical and literatural books (Munsaryu 문사류) were independentely arranged. There were the renamed orders; from Wisa(위사) to Paesa(패사), Chapsa (잡사) to Pyolsa(열사), Chapchonki(잡전기) to Chonki(전기), Ryusoh(류서) to Ryusa(류서). Introduction had only main preface. The books of each subject catalogued by title, the volume number, and author and arranged mainly by authors. Annotations were written consecutively after title and the volume number. In the afternote the number of not-treated books were revealed. Difference from Singdangsohyemunji(신당서예문지) were that the concept and boundary of orders became more clearer. It also wrote the number of books consecutive to main subject. 6) Modelling on Chonkyongdangsomok (경당서목), Myongsayemunji(명사예문지) was compiled in the basis of books and documents published in the Ming Danasty. In classification system, Myongsayemunji partly merged and the seperated some orders for it. It also deleted and renamed some of orders. In case of necessity, combining of orders' norm was occured particulary in the part of Sabu(사부) and Chabu(자부). Therefore these merging of orders norm didn't offer sufficient theretical background. For example, such demerits were seen in the case that historical books edited by annals were combined with offical historical ones which were differently compiled and edited from the former. In the part of Chabu(자부), it broke out another confusion that Pubga(법가), Meongga(명가), Mukga(묵가), Chonghweongka's(종횡가) thoughts were classified in the Chapka(잡가). Scriptures of Taiosim and Buddhism were seperated from each other. There were some deleted books such as Mokrokryu(목록류), Paesaryu(패사류) in the part of history (Sabu 사부) and Chosaryu(초사류) in the part of Chipbu(집부). The some in the each orders had been renamed. Imitating compiling system of Songsayemunji(송사예문지), with reffering to its differ-ence, Myongsayemunji(명사예문지) wrote the review and the change of the books by author. The number of not-treated books didn't appear at the total. It also deleted the total following main subject.
The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.