• Survey Research
• /
• v.11 no.2
• /
• pp.1-28
• /
• 2010

This paper reports the results of the 2009 Internet volunteer panel version of the social survey conducted by Statistics Korea (Korea National Statistical Office). Authors identify socio-psychological characteristics of Internet survey volunteers and present quantitative evaluation of the propensity adjustment weighting method intended to remove Internet sample bias. The nine criteria used for propensity adjustment were regions, urban/rural, gender, age, education, consumer satisfaction, views on income distribution, newspaper access and Internet news access. Propensity adjustment weighting based on the logit model and rim weights were applied to the online survey of 2,903 respondents using the face-to-face area sample data of 37,049 respondents as reference. A total of 106 items were used for evaluating the propensity adjustment weighting methods. The results showed that in 80% of survey items the propensity adjustment weighting yielded better estimates compared to simple demographic weighting. This suggests that Internet surveys by volunteer panels are useful for conducting the general social study in Korea. The reference survey data for this study contains several items on social-psychological behaviors and attitudes, is large in size and obtained by probability sampling. Thus it may be utilized in propensity adjustment of other Internet surveys.

• Journal of Digital Convergence
• /
• v.14 no.9
• /
• pp.159-170
• /
• 2016

In this study, the authors examine the ways in which social media ultimately affects the attachment of individuals to their favorite brands. Through an analysis of data using multiple linear regression, this study finds that SNS (social networking site) users that post status updates for a wide audience have no bearing on the individual's use of socialization agents. Those who consume social news passively are, however, likely to depend on socialization agents for determining their final purchase decisions. Socialization agents, both personal and non-personal, also play a role in the formation of brand attachment among individuals who depend on these social sources. Based on these results, marketers are encouraged to establish an online footprint of a social nature to formulate brand awareness and to provide a means for users of social media to improve their brand attachments to their favored brands. As this research was conducted exclusively in the predominantly collective culture of South Korea, further studies could attempt to analyze social networking use and socialization agent use via a cross-culture study, particularly one including an individualistic culture.

• The Korean Journal of Financial Management
• /
• v.26 no.3
• /
• pp.103-138
• /
• 2009

In this article we examine a unique data set of intraday fair disclosure(FD) releases to shed light on market efficiency within the trading day. Specifically, this paper analyze the response of stock prices on fair disclosure disseminated in real-time through KIND(Korea Investor's Network for Disclosure) on Korea stock exchange during the period from January 2003 to September 2004. We find that the prices of stock experiences a statistically and economically significant increase beginning seconds after the fair disclosure is initially announced and lasting approximately two minutes. The stock price responds more strongly to fair disclosure on smaller firm but the response to fair disclosure on the largest firm stock is more gradual, lasting five minutes. We also examine the profitability of a short-term trading strategy based on dissemination of fair disclosure. After controlling for trading costs we find that trader who execute a trade following initial disclosure generate negative profits, but trader buying stock before initial disclosure realize statistically significant positive profit after two minute of disclosure. Summarizing overall results, our evidence supports that security prices on Korea stock exchange reflects all available information within two minutes and the Korea stock market is semi-strongly efficient enough that a trader cannot generate profits based on widely disseminated news unless he acts almost immediately.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.191-206
• /
• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 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 Distribution Science
• /
• v.10 no.4
• /
• pp.47-53
• /
• 2012

Businesses make use of advertising strategy using models to give consumers efficient product information. Modern advertisements often make use of models for greater reminiscence to create messages and remind viewers of the product. The purpose of this study was to examine the characteristics of each type of model. The subjects were 230 college students in their twenties or older, and the material was collected from October 20, 2011 to November 5, 2011 to examine the effects of model characteristics on buying intention as well as attitude toward a brand. A questionnaire survey was used; investigators gave one copy to each interviewee. The study investigated the characteristics of each model using a questionnaire of each 40 copies with five kinds of photographs. The characteristics of models had great influence on buying intention and attitude toward the brand: First, factor 2 (being honest and virtuous and having good credit and a good press assessment) and factor 3 (being interesting and a good communicator and creating good memories) had great influence on buying intention. Factor 2 was explained by reliability, and factor 3 by the efficiency of the model in creating a feeling. Second, factors 1 (being attractive, smart, unique, friendly, loved by others, and popular), 2, and 3 influenced attitude toward brand. Factor 1 encapsulated the outgoing characteristics of a model, factor 2 was based on reliability, and factor 3 was based on the efficiency of the model in creating a feeling. The model's positive effects on buying intention and attitudes toward brand shall be examined. For their positive influence on buying intention, reliability and efficiency shall be given attention. For their positive influence on attitude toward brand, creating a good impression, having outgoing characteristics, being reliable, and efficiency shall be given attention. The findings were as follows: Model characteristics influencing buying intention were similar to those influencing attitude toward brand. The differences were as follows. First, reliability and efficiency influenced buying intention. When customers were asked to consider the influence on buying intention of an advertisement, regardless of the strength of the buying intention, they considered these two characteristics. Customers decided to buy based not only on the credibility of the product as presented in the advertisement but also the transmission of the contents of the advertisement. Second, outgoing characteristics, reliability, and efficiency influenced attitude toward a brand. The attitude toward a brand was said to be the attitude toward the business. The attitude is produced even after buying, so businesses view it as very important. The attitude might vary depending upon the model used rather than the brand. Therefore, a model with outgoing characteristics was thought to be important. Therefore, attitude toward a brand whose model influenced buying intention as well as attitude toward brand had outgoing characteristics. The result is that an image the model was related to attitude toward the brand. As such, customers would buy the goods advertised. However, an outgoing image of a model was also important to create a positive attitude toward a business brand. For instance, talent Park Gyeong-Rim's photo was used to promote cosmetics about 10 years ago. When she worked as a model of cosmetics products, she had to make compensation for losses and damages because she made a mistake on a talk show program. At that time, customers who had bought the cosmetics product asked for refunds of several billion won. As such, models who are said to be the face of the businesses they represent can play an important role. To advertise in the most attractive and effective way, the current image of a model should be investigated by examining current activities and news articles after selecting the model, and the model's efficiency and attitude toward the brand should be examined. Factors that stimulate customers' buying decisions can be used to plan advertisement that have positive influence on a brand. This study had the limitation of investigating mainly college students and there were insufficient copies of the questionnaire. The investigation was not done widely but in detail so that a concrete investigation could not be done. Further studies shall supplement these shortcomings and discuss new directions.

• The Korean Journal of Archival Studies
• /
• no.22
• /
• pp.77-114
• /
• 2009

The right to know is not satisfied merely by making or improving laws or systems. The right to know is a matter of culture rather than system. Nevertheless, consistent system improvement measures are required. There are many laws relating to the right to know. In particular, at the core are the Official Information Disclosure Act, the Record and Archives Management Act, and the Presidential Record Management Act. The fact that systems relating to official record management and presidential record management are related to the right to know is understood by the promotion of records and archives management reform after the year 2004, as a result of which the national archives management innovation road map was established. Reflecting the many opinions of the "Information Disclosure System Improvement Task Force" composed with participation of the government and the press after the participatory government's announcement of "Measures to Advance the Support System for News Coverage," amendments to the Information Disclosure Act have come forward with system improvement measures in connection with issues that had arisen until then. Such improvement measures have not resulted in actual improvements. This thesis proposes several system improvement measures, focusing on those that have arisen until now but have not been reflected in discussion, such as converting the concept of information non-disclosure into disclosure postponement, preparing and disclosing particular information disclosure standards, specifying personal information for non-disclosure, specifying and strictly applying any information that has not been disclosed for purposes of internal review, deleting non-disclosure items in stenographic records that do not have a reason to exist, and establishing limits and terms of non-disclosure. Of the most remarkable system improvement measures that have been made until now is our recognition that the right to know is not limited to the information disclosure system but that the "cause" of archive management should be systematic and scientific. In other words, the right to know is understood to establish not just accidential factors, such as with a whistle-blower, but the inevitable factors of systemization of production, distribution, preservation, and use of archives. Much more study should be pursued regarding disclosure of archives information. In particular, difficult issues to be resolved regarding reading records at permanent archives management institutions, such as the National Archives of Korea, or copyrights that arise in the process, require constant study from academia and relevant institutions.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.8
• /
• pp.91-106
• /
• 2021

The purpose of this study is to define the process of leading to self-realization and social solidarity through the process of contents planning, production, and distribution as Contents Activism, and to categorize specific execution steps. Based on this, we try to analyze concrete cases to find out the social meaning and effect of the practice of Contents Activism. As for the research method, after examining the differences between traditional activism and Contents Activism through a review of previous studies, the implementation process of Contents Activism was categorized into 7 steps. By applying this model, this study analyzed two cases of Contents Activism. The first case is the 100 laps challenge in the backyard planned by an elderly man ahead of his 100th birthday in early 2020, when the fear of COVID-19 spread. Sir Tom Moore, who lives in the UK, challenged to walk 100 laps in the backyard to help medical staff from the National Health Service as COVID-19 infections and deaths increased due to a lack of protective equipment. His challenge, which is difficult to walk without assistive devices due to cancer surgery and fall aftereffects, drew sympathy and participation from many people, leading to global solidarity. The second case analyzes the case of 'The Unfreedom of Expression, Afterwards' by Kim Seo-kyung and Kim Woon-seong, who were invited to the 2019 Aichi Triennale special exhibition in Japan. The 'Unfreedom of Expression, After' exhibition was a project to display the Statue of Peace and the lives of comfort women in the Japanese military, but it was withdrawn after three days of war due to threats and attacks from the far-right forces. Overseas artists who heard this news resisted the Triennale's decision, took and shared photos in the same pose as the Statue of Peace on social media such as Twitter and Instagram, empathizing with the historical significance of the Statue of Peace. Activism, which began with artists, has expanded through social media to the homes, workplaces, and streets of ordinary citizens living in various regions. The two cases can be said to be Contents Activism that led to social practice while solidifying and communicating with someone through contents.

• Journal of Distribution Research
• /
• v.17 no.5
• /
• pp.1-23
• /
• 2012

The conflict between major corporations and small and medium-sized businesses is being aggravated, the trickle down effect is not working properly, and, as the controversy surrounding the effectiveness of the business limiting system continues to swirl, the plan proposed to protect the business domain of small and medium-sized businesses, resolve polarization between these businesses and large corporations, and protect small family run stores is the suitable business type designation system for small and medium-sized businesses. The current status of carrying out this system of selecting suitable business types among small and medium-sized businesses involves receiving applications for 234 items among the suitable business types and items from small and medium-sized businesses in manufacturing, and then selecting the items of the consultative group by analyzing and investigating the actual conditions. Suitable business type designation in the service industry will involve designation with priority on business types that are experiencing social conflict. Three major classifications of the service industry, related to the livelihood of small and medium-sized businesses, will be first designated, and subsequently this will be expanded sequentially. However, there is the concern that when designated as a suitable business type or item, this will hinder the growth motive for small to medium-sized businesses, and designation all cause decrease in consumer welfare. Also it is highly likely that it will operate as a prior regulation, cause side-effects by limiting competition systematically, and also be in violation against the main regulations of the FTA system. Moreover, it is pointed out that the system does not sufficiently reflect reverse discrimination factor against large corporations. Because conflict between small to medium sized businesses and large corporations results from the expansion of corporations to the service industry, which is unrelated to their key industry, it is necessary to introduce an advanced contract method like a master franchise or local franchise system and to develop local small to medium sized businesses through a franchise system to protect these businesses and dealers. However, this method may have an effect that contributes to stronger competitiveness of small to medium sized franchise businesses by advancing their competitiveness and operational methods a step further, but also has many negative aspects. First, as revealed by the Ministry of Knowledge Economy, the franchise industry is contributing to the strengthening of competitiveness through the economy of scale by organizing existing individual proprietors and increasing the success rate of new businesses. It is also revealed to be a response measure by the government to stabilize the economy of ordinary people and is emphasized as a 'useful way' to revitalize the service industry and improve the competitiveness of individual proprietors, and has been involved in contributions to creating jobs and expanding the domestic market by providing various services to consumers. From this viewpoint, franchises fit the purpose of the suitable business type system and is not something that is against it. Second, designation as a suitable business type may decrease investment for overseas expansion, R&D, and food safety, as well negatively affect the expansion of overseas corporations that have entered the domestic market, due to the contraction and low morale of large domestic franchise corporations that have competitiveness internationally. Also because domestic franchise businesses are hard pressed to secure competitiveness with multinational overseas franchise corporations that are operating in Korea, the system may cause difficulty for domestic franchise businesses in securing international competitiveness and also may result in reverse discrimination against these overseas franchise corporations. Third, the designation of suitable business type and item can limit the opportunity of selection for consumers who have up to now used those products and can cause a negative effect that reduces consumer welfare. Also, because there is the possibility that the range of consumer selection may be reduced when a few small to medium size businesses monopolize the market, by causing reverse discrimination between these businesses, the role of determining the utility of products must be left ot the consumer not the government. Lastly, it is desirable that this is carried out with the supplementation of deficient parts in the future, because fair trade is already secured with the enforcement of the franchise trade law and the best trade standard of the Fair Trade Commission. Overlapping regulations by the suitable business type designation is an excessive restriction in the franchise industry. Now, it is necessary to establish in the domestic franchise industry an environment where a global franchise corporation, which spreads Korean culture around the world, is capable of growing, and the active support by the government is needed. Therefore, systems that do not consider the process or background of the growth of franchise businesses and harm these businesses for the sole reason of them being large corporations must be removed. The inhibition of growth to franchise enterprises may decrease the sales of franchise stores, in some cases even bankrupt them, as well as cause other problems. Therefore the suitable business type system should not hinder large corporations, and as both small dealers and small to medium size businesses both aim at improving competitiveness and combined growth, large corporations, small dealers and small to medium sized businesses, based on their mutual cooperation, should not include franchise corporations that continue business relations with them in this system.

• Asia pacific journal of information systems
• /
• v.21 no.1
• /
• pp.103-122
• /
• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.