• The Korean Journal of Archival Studies
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• no.81
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• pp.5-44
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• 2024

This paper is an experience and observation report on the activities of Jeonju citizens, who are 'kleine leutes'. Text or Born-digital materials such as diaries, group chat rooms, memos, and interviews showing citizens' contemporary and daily history (Alltagsgeschichite) were used for this purpose. These civic groups are reading groups we can find easily and they also enjoy walking, hiking, and movies, and so to speak ordinary local people are their members. One team read Choi Myung-hee's "Honbul" for about a year and a half, while another team read several books under the theme of "commons," and enjoyed exploring, exhibiting, or watching movies together. The main text is composed of three parts. First, I looked at the methods and perspectives to examine the daily life of local people. To this end, the views of Detlev Peukert and Alf Lüdtke, who captured the prospects and the possibilities of theories of daily history, and James C. Scott, who provided insight into infra-politics, were reviewed. This work was to find the perspective and method of daily history research that could observe the activities of Jeonju citizens. Second, we looked at the experience of the "Honbool" meeting. The reading of "Honbool" which took place during the period of confrontation with Covid19 began in connection with its intense locality. As the criticism of "a great writer born in our local land" relieved the uncomfortable feelings, the members' critical mind was revealed after Volume3 of "Honbool" and stood out after Volume6. It seemed to show the characteristics of the self-consciousness (Eigensinn) of citizens who choose dynamics rather than being stuck to a specific form of empathy (Betroffenheit). I think it showed the difficulty and hope to face in the description and research of local history at the same time. Third, I observed citizens who gathered on the subject of public land. This meeting showed the actuality and accumulation process of the infra-political capabilities of citizens in Jeonju. Reading-commons did not suffer from 'heart trouble' as a local citizen compared to "Honbool". Rather, the difficulty of related books was an obstacle, and the difficulty was easily resolved. As the meeting progressed, awareness of the commons became more sophisticated and issues and discussions were independently shared with each other, and a wealth of hidden transcripts were accumulated through its practice and problem consciousness. It is difficult to think about modern daily life apart from the capitalist era. More fundamentally, it is here and now in everyday life that humans enjoy or suffer from. All history passes through my body here and now. This is the universality of daily history. It depends on the ability of citizens to create daily history to experience and at the same time maintain the distance of criticism.

• 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 Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Korean Biological Nursing Science
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• v.4 no.1
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• pp.25-39
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• 2002

The purpose of this study is to develop and evaluate a web-based instruction Program(WBI) to help nurses improving their knowledge and skill of cardiopulmonary resuscitation. Using the model of web-based instruction(WBI) program designed by Rhu(1999), this study was carried out during February-April 2002 in five different steps; analysis, design, data collection and reconstruction, programming and publishing, and evaluation. The results of the study were as follows; 1) The goal of this program was focused on improving accuracy of knowledge and skills of cardiopulmonary resuscitation. The program texts consists of the concepts and importances of cardiopulmonary resuscitation(CPR), basic life support(BLS), advanced cardiac life support(ACLS), treatment of CPR, nursing care after CPR treatment. And in the file making step, photographs, drawings and image files were collected and edited by web-editor(Namo), scanner and Adobe photoshop program. Then, the files were modified and posted on the web by file transfer protocol(FTP). Finally, the program was demonstrated and once again revised by the result, and then completed. 2) For the evaluation of the program, 36 nurses who in K university hospital located in D city, and related questionnaire were distributed to them as well. Higher scores were given by the nurses in its learning contents with $4.2{\pm}.67$, and in its structuring and interaction of the program with $4.0{\pm}.79$, and also in its satisfactory of the program with $4.2{\pm}.58$ respectively. In conclusion, if the contents of this WBI educational program upgrade further based upon analysis and applying of the results the program evaluation, it is considered as an effective tool to implement for continuing education as life-long educational system for nurse.

• Korean journal of communication and information
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• v.17
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• pp.112-144
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• 2001

This study attempts to analyze how and why Chosun Ilbo and Hankyoreh Shinmun produce particular social discourses about the media reform in different ways. In doing so, this paper attempts to disclose the ideological nature of media reform discourses in social contexts. For the purpose, a content analysis method was applied to the analysis of straight news, while an interpretive discourse analysis was appled to analyze both editorials and columns in newspapers. As a theoretical framework, an articulation theory was applied to explain the relationships among social forces, ideological elements, discourse practices and subjects to produce the media reform discourses. In doing so, I attempted to understand the overall conjuncture of the media reform aspects in social contexts. The period for the analysis was limited from January 10th to August 10th this year. Newspaper articles related to the media reform were obtained from the database of newspaper articles, "KINDS," produced by Korean Press Foundation, in searching the key word, "media reform". Total articles to be analyzed were 765, 429 from Hankyoreh Sinmun and 236 from Chosun Ilbo. The research results, first of all, empirically show that both Chosun Ilbo and Hankure Synmun used straight news for their firms' interests and value judgement, in selecting and excluding events related to media reform or in exaggerating and reducing the meanings of the events, although there are differences in a greater or less degree between two newspaper companies. Accordingly, this paper argues that the monopoly of newspaper subscriber by three major newspapers in Korean society could result in the forming of one-sided social consensus about various social issues through the distorting and unequal reporting by them. Second, this paper's discourse analysis related to the media reform indicates that the discourse of ideology confrontation between the right and the left produced by Chosen Ilbo functioned as a mechanism to realize law enforcement of the right in articulating the request of media reform and the anti-communist ideology. It resulted in the discursive effect of suppressing the request of media reform by civic groups and scholars and made many people to consider the media reform as a ideological matter in Korean society.

• Sijohaknonchong
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• v.21
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• pp.77-110
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• 2004

This study is an exploration of a Korean-American writer, Hong-Eun's Mong-yu siga. Hong-Eun immigrated to the United States during the colonial rule of the Japanese government. He was a publisher of The New Korea Times, and contributed various literary works to it. The purpose of this study is to analyze his two Mong-yu sigas published in 1935 and 1947 and elucidate their meanings. Using dream as a primary motif, the intention of Mong-yu mode is to achieve desire which is impossible to reach in reality. While his staying in the United States, Hong-Eun could not return his home country for two reasons, that is, political and financial ones. To return Korea desperately, he wrote sigas by adopting Mong-yu mode. His first attempt was reflected as eight pieces of consecutive poetries titled This Mountain In My Dream, I am Home. This Mountain was published on the 25th of April, 1935 and In My Dream, I am Home was contributed from May the 9th of 1935 to July the fourth of the same year. These works were published in the The New Korea Times' poetry column under the pen name of Donghae-soboo , Representing gasa of the enlightenment era, this poetry depicts historical identity of Chosun dynasty, especially focusing on before and after the 1900s. As a result of it, the poetry sketches the ideology of the Middle Ages. His second attempt was A Country and Hometown written as a form of prelude on the 25th of September, 1947. In addition, A Country in My Dream was published as a form of six pieces of consecutive poetry from October the second to November the sixth of 1947. He chose sijo as a major form of poetry, and the image of the poetry seemed to be the continuation of his first attempt. Confronting the reality of the his own country which is divided, the writer expresses his antagonism toward America and Russia. Although he could eventually return his country later, he rationalized himself by saying that his it is not the ideal place to go. Mong-yu mode is a traditional poetic technique which the intellectuals of the Middle Age used to use as one pattern of allegory. In addition to this, in the period of the enlightenment of Korea, Mong-yu was used to avoid the Japanese censorship and experiment on the diverse ways of writing. In terms of literary history, the significance of Hong-Eun's creation of Mong-yu sigas is that Hong-Eun shares the same intention with Korean intellectuals of the enlightenment period.

• Cartoon and Animation Studies
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• s.47
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• pp.81-101
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• 2017

The present study argues that film director Lee Ae-rim animation works depict the world of the grotesque and not only are important sociocultural phenomena but also hold the significance of humor and subversion. The grotesque exhibits the intriguing characteristics of expressing a perspective critical of the existing society through the sensibilities of minorities and is used broadly as a term not only in the aesthetic sense but also designating sociocultural phenomena. Although discussed separately in terms of Mikhail Bakhtin's carnival grotesque and Mary Russo's uncanny grotesque, the grotesque fundamentally rejects existing order and conventions and is externalized through unique expressions, thus opening up a rich possibility for rejection, humor, satire, transformation, and deconstruction of and regarding the authority of the mainstream. Although they constitute a fictional medium, animation films are social products as well so that they are affected by society, culture, and history and stand as important texts that must be interpreted in terms of the relationships between humans' instinctive desires and society and between the overall culture and artistic media. However, the rarity of grotesque portrayals in South Korean animation films also proves that it is a society where even problems that are in themselves sensitive must be manifested ingeniously on a conventional level. South Korean society has a unique history of colonialism and national division and is simultaneously in the unique situation of a society that has undergone growth at a nearly unprecedented rate. Consequently, the society exhibits closed yet dynamic particularity where everyday tension and rigidity, wariness of others and extreme competition are intertwined in a complex manner. Intensively analyzed in the present discussions, director Lee's animation films and are characterized mainly by grotesque images, nonlinear narratives, and vivid depictions. In such a context, these works not only are artistic products of South Korean society but also rejections of a rigid society and share the significance of the aesthetics of the carnival grotesque, which consists of subversive expressions directed at a new world.

• Journal of Food Hygiene and Safety
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• v.31 no.3
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• pp.153-166
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• 2016

The purpose of this study was to investigate consumers' perceptions on monosodium L-glutamate (MSG) in social media. Data were collected from Naver blogs and Naver web communities (Korean representative portal web-site), and media reports including comment sections on a Yonhap news website (Korean largest news agency). The results from Naver blogs and Naver web communities showed that it was primarily mentioned MSG-use restaurant reviews, 'MSG-no added' products, its safety, and methods of reducing MSG in food. When TV shows on current affairs, newspaper, or TV news reported uses and side effects of MSG, search volume for MSG has increased in both PC and mobile search engines. Search volume has increased especially when TV shows on current affairs reported it. There are more periods with increased search volume for Mobile than PC. Also, it was mainly commented about safety of MSG, criticism of low-quality foods, abuse of MSG, and distrust of government below the news on the Yonhap news site. The label of MSG-no added products in market emphasized "MSG-free" even though it is allocated as an acceptable daily intake (ADI) not-specified by the Joint FAO/WHO Expert Committee on Food Additives (JECFA). When consumers search for MSG (monosodium L-glutamate) or purchase food on market, they might perceive that 'MSG-no added' products are better. Competent authorities, offices of education and local government provide guidelines based on no added MSG principle and these policies might affect consumers' perceptions. TV program or news program could be a powerful and effective consumer communication channel about MSG through Mobile rather than PC. Therefore media including TV should report item on monosodium L-glutamate with responsibility and information based on scientific background for consumers to get reliable information.