• Women's Studies Review
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• v.28 no.1
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• pp.3-42
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• 2011

This study aims to analyze the characteristics of transnational cigarette companies' strategies to expand the female market in Korea after the market opening of 1988. Focusing on 'Virginia Slims' and 'Finesse', which are the PM and BAT's representative female brands, this study explores whether their typical advertising strategies were transformed in Korean market. After 3years from the market opening, 'Virginia Slims' gave up its brand identity and the strategy was so successful that 'Virginia Slims' continued to be 2nd best-selling brand in the imported cigarette market in Korea. In contrast, 'Finesse' maintained a typical women's cigarette image during the 1990s and consequently occupied the highest brand awareness as a female cigarette but lower market share than Virginia Slims. TTCs adapted to a doubly obstructed Korea market with its strong taboo against female smoking and a comparatively stronger legal ban on all cigarette ads targeting women. However, diverse indirect cigarette ads and promotions, which circumvented regulations, suggest that ads transforming was not to give up Korean female customers. Furthermore, the cigarette ads that the soft and mild taste of female brands are associated with healthy image rather than gendered image may appeal to Korean women without touching their emotions and desires.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.257-280
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• 2022

In this study, we analyzed comments on news articles of representative companies of the three industries (i.e., semiconductor, secondary battery, and bio industries) that had been listed as national strategic technology projects of South Korea to identify public opinions towards them. In addition, we analyzed the relationship between changes in public opinion and stock price. 'Samsung Electronics' and 'SK Hynix' in the semiconductor industry, 'Samsung SDI' and 'LG Chem' in the secondary battery industry, and 'Samsung Biologics' and 'Celltrion' in the bio-industry were selected as the representative companies and 47,452 comments of news articles about the companies that had been published from January 1, 2020, to December 31, 2020, were collected from Naver News. The comments were grouped into positive, neutral, and negative emotions, and the dynamic topics of comments over time in each group were analyzed to identify the trends of public opinion in each industry. As a result, in the case of the semiconductor industry, investment, COVID-19 related issues, trust in large companies such as Samsung Electronics, and mention of the damage caused by changes in government policy were the topics. In the case of secondary battery industries, references to investment, battery, and corporate issues were the topics. In the case of bio-industries, references to investment, COVID-19 related issues, and corporate issues were the topics. Next, to understand whether the sentiment of the comments is related to the actual stock price, for each company, the changes in the stock price and the sentiment values of the comments were compared and analyzed using visual analytics. As a result, we found a clear relationship between the changes in the sentiment value of public opinion and the stock price through the similar patterns shown in the change graphs. This study analyzed comments on news articles that are highly related to stock price, identified changes in public opinion trends in the COVID-19 era, and provided objective feedback to government agencies' policymaking.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.90-103
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• 2009

The poem 'Kyungjusipiyung(慶州十二詠)' written by Seo, Geo-jeong(徐居正) describes sentiments felt for the ruined historical and cultural landscape of Silla's capital city, Kyungju. It differs from the existing 'Eight Sceneries(八景)' as it conveys the strong metaphorical aesthetics of ruins as the episodes and figures are sung, as well as the myths and stories related to the representative holy places of the Silla culture: Gyelim(鷄林), Banwolseong(半月城), Najeong(蘿井), Oneung(五陵), Geumosan(金鰲山), the scenic beauty of deep placeness, Poseokjeong(鮑石亭), Mooncheon(蚊川), Cheomseongdae(瞻星臺), Boonhwangsa(芬皇寺), Youngmyosa(靈妙寺) and Grave of the General Kim Yu-Sin(金庾信墓). Compared with the former "Eight Sceneries" Poems, including Seo Geojeong's 'Kyungjusipiyung', there is a difference in the content of theme recitation, as well as in structure and form, especially with the deep impression of the classical features of the meanings and acts. The sequence of theme recitation seems to be composed of more than two visual corridors visited during trips that last longer than two days. The dominant emotions expresses in this poem, through written in the spring, are regret and sadness such as 'worn', 'broken and ruined', 'old and sad', without touching on the beauty of nature and the taste for life that is found in most of the Eight Sceneries Poems. Thus, the feelings of the reciter himself, Seo, Geo-jeong, about the described sceneries and their symbolism are more greatly emphasized than the beauty of form. The characteristic aspect of his experiences of ruins expressed from 'Kyungjusipiyung' is that the experiences were, first of all, qualitative of the aura conveyed; that is, the quality omnipresent throughout the culture of Silla as reflected in the twelve historical and cultural landscapes. In this poem, the cultural ruins of the invisible dimension such as the myths and legends are described by repetition, parallelism, juxtaposition, reflection and admiration from the antiphrases, as well as the civilized ruins of the visible dimension such as the various sceneries and features of Kyungju. This seems to be characteristic of the methods by which Seo, Geo-jeong appreciates 'Silla' in the poem 'Kyungjusipiyung'. Ruins as an Aesthetic Object imply the noble pride of Seo, Geo-jeong in identifying himself with the great nature of ruins. In 'Kyungjusipiyung', the images of the ruins of Silla and Kyungju are interspersed in spite of his positive recognition of 'the village of Kyungju' based on his records. However, though the concept of ruins has a pessimistic tone connoting the road of extinction and downfall, the aspect here seems to ambivalently contain the desire to recover and revive Kyungju through the Chosun Dynasty as adominant influence on the earlier Chosun's literary tide. The aesthetics of the scenery found in Seo, Geo-jeong's 'Kyungjusipiyung' contain the strongest of metaphor and symbolism by converting the experiences of the paradoxical ruins into the value of reflective experiences.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.45-67
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• 2016

Given the recent development of smart devices, users are producing, sharing, and acquiring a variety of information via the Internet and social network services (SNSs). Because users tend to use multiple media simultaneously according to their goals and preferences, domestic SNS users use around 2.09 media concurrently on average. Since the information provided by such media is usually textually represented, recent studies have been actively conducting textual analysis in order to understand users more deeply. Earlier studies using textual analysis focused on analyzing a document's contents without substantive consideration of the diverse characteristics of the source medium. However, current studies argue that analytical and interpretive approaches should be applied differently according to the characteristics of a document's source. Documents can be classified into the following types: informative documents for delivering information, expressive documents for expressing emotions and aesthetics, operational documents for inducing the recipient's behavior, and audiovisual media documents for supplementing the above three functions through images and music. Further, documents can be classified according to their contents, which comprise facts, concepts, procedures, principles, rules, stories, opinions, and descriptions. Documents have unique characteristics according to the source media by which they are distributed. In terms of newspapers, only highly trained people tend to write articles for public dissemination. In contrast, with SNSs, various types of users can freely write any message and such messages are distributed in an unpredictable way. Again, in the case of newspapers, each article exists independently and does not tend to have any relation to other articles. However, messages (original tweets) on Twitter, for example, are highly organized and regularly duplicated and repeated through replies and retweets. There have been many studies focusing on the different characteristics between newspapers and SNSs. However, it is difficult to find a study that focuses on the difference between the two media from the perspective of supply and demand. We can regard the articles of newspapers as a kind of information supply, whereas messages on various SNSs represent a demand for information. By investigating traditional newspapers and SNSs from the perspective of supply and demand of information, we can explore and explain the information dilemma more clearly. For example, there may be superfluous issues that are heavily reported in newspaper articles despite the fact that users seldom have much interest in these issues. Such overproduced information is not only a waste of media resources but also makes it difficult to find valuable, in-demand information. Further, some issues that are covered by only a few newspapers may be of high interest to SNS users. To alleviate the deleterious effects of information asymmetries, it is necessary to analyze the supply and demand of each information source and, accordingly, provide information flexibly. Such an approach would allow the value of information to be explored and approximated on the basis of the supply-demand balance. Conceptually, this is very similar to the price of goods or services being determined by the supply-demand relationship. Adopting this concept, media companies could focus on the production of highly in-demand issues that are in short supply. In this study, we selected Internet news sites and Twitter as representative media for investigating information supply and demand, respectively. We present the notion of News Value Index (NVI), which evaluates the value of news information in terms of the magnitude of Twitter messages associated with it. In addition, we visualize the change of information value over time using the NVI. We conducted an analysis using 387,014 news articles and 31,674,795 Twitter messages. The analysis results revealed interesting patterns: most issues show lower NVI than average of the whole issue, whereas a few issues show steadily higher NVI than the average.

• Sijohaknonchong
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• v.42
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• pp.213-238
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• 2015

Jeongun(丁芸) Lee, Young-do (李永道), who is deemed a representative female poet of Korea, began her literary career in May, 1946 when she published in a publication called "Bamboo Sprout, (죽순)". Her Korean identity, which was formed through her Confucius upbringing as well as traditional value system of her family, had a strong presence in her work, and she remained a quintessential figure in Korea's female sijo poet circle for 30 years until her passing in 1976. Despite the highly acclaimed talent and her noble aspirations, it is undeniable that her works did not receive fair assessment due to her private life. Against this backdrop, it is necessary to deeply inquire the literary values and beauty of Young-do Lee's sijo. As mentioned, Young-do Lee is a solidly established figure in Korea's modern poetry. The following illustrates the spirit and the world of her poetry. First, Young-do Lee lived through turbulent times and it was her country that served as the source of her sijo work. Assessing the multitude of dramatic historical events such as Japanese colonization, 8.15 Liberation of Korea, division of the nation, 6.25 Korean war, 4.19 Revolution, 5.16 military coup, it is natural that patriotism was strongly present in her work who was one of the intellectuals at the time. Second, Young-do Lee is a poet who had experienced more pain than others in terms of the turbulence of the time. Her Korean identity, which was formed through her Confucius upbringing as well as traditional value system of her family, had a strong presence in her work. Third, Jeongun Lee, Young-do is a poet of longing. The abundance and richness of her emotions were fortified through the relationship with another poet, Chihwan Yu. Fourth, Young-do Lee is a poet opened up new horizons for the modennization. The transparency of image reflected in her work and the elaborate nature of her language are outstanding. In summary, Young-do Lee was a true artist, who has a strong presence in Korea's modern poetry society, and who was a poet of patriotism, poet who suffered the turbulence of the times, and a poet of longing.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• (The)Study of the Eastern Classic
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• no.72
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• pp.35-66
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• 2018

This thesis has examined Jo Guimyeong's observation method and rhetoric of style of writing style. He tried to look at the world differently through observation and expressed relationship with the world through the style of comic. $J{\breve{o}}nggwan$ is a new way of looking at subjects and objects. It trust the senses and thoughts of the subject. So It is to clarify the circumstances and logic of the world from one's own point of view. In this case, it collides with the common thinking of the day. He put the reason and the action standard in the "taste" and the "mind". This means three things. First, he is proud that his reasons and actions are no different from those of a saint. Second, an individual is an independent being with different emotions and thoughts. Third, based on this, his works of literature have their own value. These reasons and actions were incarnated through '$J{\breve{o}}nggwan$(靜觀)'s observation methods. What he gained from the three stages of $J{\breve{o}}nggwan$(靜觀)' is the 'great mind'. The first step is self-reflection. It is the process of objectifying oneself. The second target is the appearance of things. It's about looking at everything equally, whether it's precious or vulgar. The third object of observation is a harmonic. He is joining the movement of the harmonizers. Therefore, one's own reasons, actions, and works of literature share the same meaning as those of a harmonizer. He said that the description can change according to his own knowledge. It means that you can fit the situation. A typical example was the analysis of 'Sung Bo hyung hwasangchan'<成甫兄畵像贊>. He described Park Moon-soo's life as the lives of officials through comic. Through this, He criticized Park Moon-soo's natural nature of the academic world. but the situation in which he can't escape from bureaucratic life by inducing laughter. This style of writing is one of the most representative features which was written by Jo Guimyeong writer.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.