• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.130-138
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• 2015

Korean movies with a great variety of styles have recently succeeded in drawing people's attention Rule number one is "the more entertainment, the more audiences". On the top of box offices are always the movies that have a number of spectacular scenes, pounding sounds and big budget characters. Hong Sang Su movies, however, receive attention from the public without above noted descriptions. One of his effective methods of expression is "realism" He has illustrated the trivial episodes with his own wits and ways. Not only has his style become famous and popular but he has many followers. He enjoys using ordinary emotion and sentiment that are likely to happen to everyone. This paper uses two tools. One is the traditional analysis of realism. The other is how he can approach the public meaningfully by listing common elements he frequently uses in his movies. Nevertheless, we can't fully understand his realism by explaining how he shows our everyday life and how much his movie looks like it. Though admitting there are so many preceding papers on his works but this paper will try to look deep into his realism through the analysis of Mise-en-Sc$\grave{e}$ne.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.383-393
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• 2018

The purpose of this paper is to study on the elements of the Sinmyung and the expression recognition of body movement in Korean dance of college students majoring in musical. The participants were 12 male and female college students in musical major who experienced in dance, song and acting. The program was composed of the experience of the Sinmyung: recognition of sound and dance, breathing and movement in the Korean dance, 8 hours twice a week for four weeks. As a qualitative approach is the discovery of the center of the process, we carried out an inductive analysis of the area on the basis of observation, in-depth interview and student report. The core of this analysis is to attempt to analyze contents concentrating on the recognition exploration of the Sinmyung sentiment and the body expression through sound and breathing. In conclusion, for college students majoring in musical, the expression recognition of the experience of the Sinmyung and the movement in the Korean dance contributes to the improvement of creative thinking through body perception, and the practical use of the capacity of image expression through concentration of sound and breathing. Finally, the results of this research could articulate with the value of body expression and the creative factors of college students majoring in musical.

• Economic Analysis
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• v.25 no.1
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• pp.1-33
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• 2019

This paper examines the economic factors that are related to the dynamics of the variance risk premium, and specially, which economic factors are related to the forecasting power of the variance premium regarding future index returns. Eleven general economic variables, eight interest rate variables, and eleven sentiment-associated variables are used to figure out the relevant economic variables that affect the variance risk premium. According to our empirical results, the won-dollar exchange rates, foreign reserves, the historical/implied volatility, and interest rate variables all have significant coefficients. The highest adjusted R-squared is more than 65 percent, indicating their significant explanatory power of the variance risk premium. Next, to verify the economic variables associated with the predictability of the variance risk premium, we conduct forecasting regressions to predict future stock returns and volatilities for one to six months. Our empirical analysis shows that only the won-dollar exchange rate, among the many variables associated with the dynamics of the variance risk premium, has a significant forecasting ability regarding future index returns. These results are consistent with results found in previous studies, including Londono (2012) and Bollerslev et al. (2014), which show that the variance risk premium is related to global risk factors.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.89-115
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• 2019

This study was conducted to present the stylistic differences between Arthur Conan Doyle and Agatha Christie, famous as writers of classical mystery novels, through data analysis, and further to present the analytical methodology of the study of style based on text mining. The reason why we chose mystery novels for our research is because the unique devices that exist in classical mystery novels have strong stylistic characteristics, and furthermore, by choosing Arthur Conan Doyle and Agatha Christie, who are also famous to the general reader, as subjects of analysis, so that people who are unfamiliar with the research can be familiar with them. The primary objective of this study is to identify how the differences exist within the text and to interpret the effects of these differences on the reader. Accordingly, in addition to events and characters, which are key elements of mystery novels, the writer's grammatical style of writing was defined in style and attempted to analyze it. Two series and four books were selected by each writer, and the text was divided into sentences to secure data. After measuring and granting the emotional score according to each sentence, the emotions of the page progress were visualized as a graph, and the trend of the event progress in the novel was identified under eight themes by applying Topic modeling according to the page. By organizing co-occurrence matrices and performing network analysis, we were able to visually see changes in relationships between people as events progressed. In addition, the entire sentence was divided into a grammatical system based on a total of six types of writing style to identify differences between writers and between works. This enabled us to identify not only the general grammatical writing style of the author, but also the inherent stylistic characteristics in their unconsciousness, and to interpret the effects of these characteristics on the reader. This series of research processes can help to understand the context of the entire text based on a defined understanding of the style, and furthermore, by integrating previously individually conducted stylistic studies. This prior understanding can also contribute to discovering and clarifying the existence of text in unstructured data, including online text. This could help enable more accurate recognition of emotions and delivery of commands on an interactive artificial intelligence platform that currently converts voice into natural language. In the face of increasing attempts to analyze online texts, including New Media, in many ways and discover social phenomena and managerial values, it is expected to contribute to more meaningful online text analysis and semantic interpretation through the links to these studies. However, the fact that the analysis data used in this study are two or four books by author can be considered as a limitation in that the data analysis was not attempted in sufficient quantities. The application of the writing characteristics applied to the Korean text even though it was an English text also could be limitation. The more diverse stylistic characteristics were limited to six, and the less likely interpretation was also considered as a limitation. In addition, it is also regrettable that the research was conducted by analyzing classical mystery novels rather than text that is commonly used today, and that various classical mystery novel writers were not compared. Subsequent research will attempt to increase the diversity of interpretations by taking into account a wider variety of grammatical systems and stylistic structures and will also be applied to the current frequently used online text analysis to assess the potential for interpretation. It is expected that this will enable the interpretation and definition of the specific structure of the style and that various usability can be considered.

• Management & Information Systems Review
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• v.39 no.2
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• pp.181-193
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• 2020

This study is a study that analyzes consumer perception by utilizing text mining, which is a recent issue. we analyzed the consumer's perception of Samsung Galaxy by analyzing consumer reviews of Samsung Galaxy YouTube ads. for analysis, 1,819 consumer reviews of YouTube ads were extracted. through this data pre-processing, keywords for advertisements were classified and extracted into nouns, adjectives, and adverbs. after that, frequency analysis and emotional analysis were performed. Finally, clustering was performed through CONCOR. the summary of this study is as follows. the first most frequently mentioned words were Galaxy Note (n = 217), Good (n = 135), Pen (n = 40), and Function (n = 29). it can be judged through the advertisement that consumers "Galaxy Note", "Good", "Pen", and "Features" have good functional aspects for Samsung mobile phone products and positively recognize the Note Pen. in addition, the recognition of "Samsung Pay", "Innovation", "Design", and "iPhone" shows that Samsung's mobile phone is highly regarded for its innovative design and functional aspects of Samsung Pay. second, it is the result of sentiment analysis on YouTube advertising. As a result of emotional analysis, the ratio of emotional intensity was positive (75.95%) and higher than negative (24.05%). this means that consumers are positively aware of Samsung Galaxy mobile phones. As a result of the emotional keyword analysis, positive keywords were "good", "good", "innovative", "highest", "fast", "pretty", etc., negative keywords were "frightening", "I want to cry", "discomfort", "sorry", "no", etc. were extracted. the implication of this study is that most of the studies by quantitative analysis methods were considered when looking at the consumer perception study of existing advertisements. In this study, we deviated from quantitative research methods for advertising and attempted to analyze consumer perception through qualitative research. this is expected to have a great influence on future research, and I am sure that it will be a starting point for consumer awareness research through qualitative research.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• The Research Journal of the Costume Culture
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• v.7 no.6
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• pp.129-139
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• 1999

This paper analyzes the trend of fashion and make-up in 1990s and their relevancy to each other. Based on the chronological analysis, we propose a new category for the fashion and make-up trend in 1990s, which is 1) traditional ecology period(1990∼1994), 2) versatile trial or decadent period(1995∼1997), and 3) soft landing period based on the minimal neo-ecology and romanticism(1998∼2000). Between 1990 and 1994, there was no differentiation in seasons. It appeared that spring/summer and fall/winter trend have had no big differences. At the beginning in 1990s, it was basically based on ecology concept that emphasizes the natural image. However after 1995, seasonal differences in trend are appeared and there were various make-up designs. The trends of spring/summer in 1996 could be named as color revolution period that emphasized the unique and individual expression of each person. In 1997, black, pastel, and brown colors were the result of reinterpreting the classic and sexy images of 1960s to natural and modernistic image of 1997. Purple color started to be introduced to us. In 1998, pastel tone, pink, and purple color expresses the glamorous look based on the romantic feminism. S/S of 1999 is mainly represented by minimalism and avant garde. For fall/winter trends, brown color lines make-up comes to mix with romantic image and developed into wine, orange, neon colors in 1995 and 1996. These colors were the symbol of property and sentiment. Gold make-up emphasizing the eye area was the tendency of that period. In 1997, the fear of coming end of century was expressed as decadent image. At that time, ethnic and romantic image appeared with vivid color lines, gold, red and violet. In 1998, romanticism was popular again with modernism and ethnic mood. It expressed the romantic elegant image. The trend has returned to the ecology mood again in 1999. This ecology is somewhat different from the previous ecology. It adds a sofistaiced feeling and sportic fashion. To express natural and sportic image, they choose pink blush. In coming 2000 as a new millennium, the yellow color will be main the stream to express vision, dream, and happiness in both fashion and make-up as an accent color. The minimal design and minimal tools will be used for the design and make-up, respectively. In addition, the fusion concept will dominate the fashion and make-up industry in the globalized and boundariless age. Through this paper, we hope that make-up can be accepted as a part of total fashion in its relationship with other elements such as shoes, clothes and accessory and that it can be considered as a independent art that has direct influence on people and industry.

• Journal of Distribution Science
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• v.11 no.12
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• pp.13-23
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• 2013

Purpose - There is a dominant opinion that medium and small enterprises in the Korean economy have not developed qualitatively but only towards quantitative growth and, therefore, the unbalanced structure between large enterprises and those that are medium and small has worsened. In particular, this rapid industrialization causes after-effects such as polarization as well as anti-business sentiment, the collapse of the middle class, and hostility against the establishment. The consensus contends that it is difficult for Korea to be an advanced nation without resolving these problems. This paper attempts to suggest a co-prosperity model by limiting the focus to business relations with medium and small manufacturers (with regard to investment among the various co-prosperity institutions of POSCO). These co-prosperity institutions have been established in POSCO; however, it is thought that the development of a co-prosperity model regarding investment in medium and small manufacturers will help many needy investment manufacturers. Research design, data, and methodology - This study analyzes research on the co-prosperity model, using it to examine Korean cases and foreign cases. The co-prosperity model has been continuously extended but is determined to be seriously insufficient. The purpose of this study is to develop the Korean co-prosperity model by reinterpreting it in various aspects. In order to develop the Korean co-prosperity model, this study suggests the case of the establishment of the co-prosperity model by POSCO with medium and small manufacturers with regard to investment. This model is expected to be presented to many enterprises as the future co-prosperity model. Results - To date, analysis of the co-prosperity model itself and the co-prosperity model through the case of POSCO have been suggested. As empirical studies on co-prosperity in Korea are not sufficient, successful models of co-prosperity should be developed in various aspects in future. It is expected that through this study, medium and small manufacturers would have an opportunity to find various growth engines by actively using the cooperation platform and establishing optimized competitiveness of steel material through a steel business model. The ecosystem of enterprises may evolve and be healthier by making more joint products through productive business relationships between large enterprises and those that are medium and small. From the enterprises' ecosystem viewpoint, cooperation between such businesses rather than one-way support is identified as an essential element for the security of inter-competitiveness. Conclusions - Infrastructure should be established to form a dynamic industry ecosystem not by transient efforts in co-prosperity, but by an entire culture of co-prosperity across industries. In this respect, the leading role of public institutions needs to be intensified initially. In addition, the effects of co-prosperity should be extended to blind spots of policies such as third party companies and regions. A precise co-prosperity monitoring system should be established to continuously conduct and extend these efforts.

• Information Systems Review
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• v.18 no.2
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• pp.23-38
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• 2016

Previous studies on information systems (IS) and finance suggest that information on stock message boards influence the investment decisions of individual investors. However, how information on online stock message boards influences an individual investor's buy or sell decisions is unclear. To address this research question, we investigate the relationship between a number of posts on stock message boards and order imbalance in stock markets. Order imbalance is defined as the difference between the daily sum of buy-side shares traded and the daily sum of sell-side shares traded. Therefore, order imbalance can suggest the direction of trades and the strength of the direction with trading volumes. In this regard, this study examines how the number of posts (information on stock message boards) influences order imbalance (stock trading behavior). We collected about 46,077 messages of 40 companies on the Korea Composite Stock Price Index from Paxnet, the most popular Korean online stock message board. The messages we collected were divided based on in-trading and after-trading hours to examine the relationship between the numbers of posts and trading volumes. We also collected order imbalance data on individual investors. We then integrated the balanced panel data sets and analyzed them through vector regression. We found that the number of posts on online stock message boards is positively related to prior order imbalance. We believe that our findings contribute to knowledge in IS and finance. Furthermore, this study suggests that investors should carefully monitor information on stock message boards to understand stock market sentiments.