• MISULJARYO - National Museum of Korea Art Journal
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• v.96
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• pp.89-122
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• 2019

In this paper, I argue that the Handscroll of a Sightseeing Trip to the Hangang and Imjingang Rivers by Jeong Su-yeong (1743~1831, pseudonym: Jiwujae) is a record of his private journeys to several places on the outskirts of Hanyang (present-day Seoul) and that it successfully embodies the painter's subjective perspective while boating on these rivers and going on outings to nearby mountains. Around 1796, Jeong Su-yeong traveled to different places and documented his travels in this 16-meter-long handscroll. Several leaves of paper, each of which depicts a separate landscape, are pieced together to create this long handscroll. This indicates that the Handscroll of a Sightseeing Trip to the Hangang and Imjingang Rivers reflected the painter's personal subjective experiences as he went along his journey rather than simply depicts travel destinations. The Handscroll of a Sightseeing Trip to the Hangang and Imjingang Rivers features two types of travel: boating and mountain outings on foot. Traveling by boat takes up a large portion of the handscroll, which illustrates the channels of the Hangang and Imjingang Rivers. Mountain outings correspond to the sections describing the regions around Bukhansan, Gwanaksan, and Dobongsan Mountains. Jeong Su-yeong traveled to this wide span of places not just once, but several times. The fact that the Hangang River system are not presented in accordance with their actual locations shows that they were illustrated at different points. After visiting the riversides of the Hangang and Namhangang Rivers twice, Jeong Su-yeong delineated them in fourteen scenes. Among them, the first eight illustrate Jeong's initial trip by boat, while the other six scenes are vistas from his second trip. These fourteen scenes occupy half of this handscroll, indicating that the regions near the Hangang River are painted most frequently. The scenes of Jeong Su-yeong's first boating trip to the system of the Hangang River portray the landscapes that he personally witnessed rather than famous scenes. Some of the eight scenic views of Yeoju, including Yongmunsan Mountain, Cheongsimru Pavilion, and Silleuksa Temple, are included in this handscroll. However, Jeong noted spots that were not often painted and depicted them using an eye-level perspective uncommon for illustrating famous scenic locations. The scenes of Jeong's second boating trip include his friend's villa and a meeting with companions. Moreover, Cheongsimru Pavilion and Silleuksa Temple, which are depicted in the first boating trip, are illustrated again from different perspectives and in unique compositions. Jeong Su-yeong examined the same locations several times from different angles. A sense of realism is demonstrated in the scenes of Jeong's first and second boating trips to the channels of the Hangang River, which depict actual roads. Furthermore, viewers can easily follow the level gaze of Jeong from the boat. The scenes depicting the Imjingang River begin from spots near the Yeongpyeongcheon and Hantangang Rivers and end with places along the waterways of the Imjingang River. Here, diverse perspectives were applied, which is characteristic of Imjingang River scenes. Jeong Su-yeong employed a bird's-eye perspective to illustrate the flow of a waterway starting from the Yeongpyeongcheon River. He also used an eye-level perspective to highlight the rocks of Baegundam Pool. Thus, depending on what he wished to emphasize, Jeong applied different perspectives. Hwajeogyeon Pond located by the Hantangang River is illustrated from a bird's-eye perspective to present a panoramic view of the surroundings and rocks. Similarly, the scenery around Uhwajeong Pavilion by the Imjingang River are depicted from the same perspective. A worm's-eye view was selected for Samseongdae Cliff in Tosangun in the upper regions of the Imjingang River and for Nakhwaam Rock. The scenes of Jeong Su-yeong's mountain outings include pavilions and small temple mainly. In the case of Jaeganjeong Pavilion on Bukhansan Mountain, its actual location remains unidentified since the pavilion did not lead to the route of the boating trip to the system of the Hangang River and was separately depicted from other trips to the mountains. I speculate that Jaeganjeong Pavilion refers to a pavilion either in one of the nine valleys in Wooyi-dong at the foot of Bukhansan Mountain or in Songajang Villa. Since these two pavilions are situated in the valleys of Bukhansan Mountain, their descriptions in written texts are similar. As for Gwanaksan Mountain, Chwihyangjeong and Ilganjeong Pavilions as well as Geomjisan Mountain in the Bukhansan Mountain range are depicted. Ilganjeong Pavilion was a well-known site on Gwanaksan that belonged to Shin Wi. In this handscroll, however, Jeong Su-yeong recorded objective geographic information on the pavilion rather than relating it to Shin Wi. "Chwihyangjeong Pavilion" is presented within the walls, while "Geomjisan Mountain" is illustrated outside the walls. Handscroll of a Sightseeing Trip to the Hangang and Imjingang Rivers also includes two small temples, Mangwolam and Okcheonam, on Dobongsan Mountain. The actual locations of these are unknown today. Nevertheless, Gungojip (Anthology of Gungo) by Yim Cheonsang relates that they were sited on Dobongsan Mountain. Compared to other painters who stressed Dobong Seowon (a private Confucian academy) and Manjangbong Peak when depicting Dobongsan Mountain, Jeong Su-yeong highlighted these two small temples. Jeong placed Yeongsanjeon Hall and Cheonbong Stele in "Mangwolam small temple" and Daeungjeon Hall in front of "Okcheonam small temple." In addition to the buildings of the small temple, Jeong drew the peaks of Dobongsan Mountain without inscribing their names, which indicates that he intended the Dobongsan peaks as a background for the scenery. The Handscroll of a Sightseeing Trip to the Hangang and Imjingang Rivers is of great significance in that it embodies Jeong Su-yeong's personal perceptions of scenic spots on the outskirts of Hanyang and records his trips to these places.

• (The)Study of the Eastern Classic
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• no.49
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• pp.73-107
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• 2012

Yeoheon Jang Hyeongwang(1554-1637), one of the greatest Mid-Joseon Confucianists did systematic studies on universe and nature. It can be considered that he inherited the academic tradition of Cho Sik (曺植) and Jeong Gu(鄭逑) and followed their steps of fengshui (風水) and compilation of geographical records. His living and thought and deserve researching with regard to geographical studies. This paper attempts to analyze Yeoheon's recognition of geography in general. In other words, I shall prove that his view of geography is Neo-Confucian. At the same time, I shall discuss how he named people's residence, how he understanded the Joseon territory, what he thought about fengshui, and what significance the complication of geographical records by his disciples had. Yeoheon considered that land is composed of water, fire, earth, and rock, and understanded the land according to the theory of Zhouyi (周易). He analyzed geographic environments by the system of Zhouyi. His study of geography is basically intended for practical use, and as a result is necessary for people to choose where to live and where to cultivate. In his opinion, it is essential to divide the land of the Joseon by means of geographical differences in order to help people to find a better place to live. We can see his Confucian view from the fact that he placed a greater emphasis on human beings over nature. Therefore, the practical use for humans is the first priority in his study of geography. Meanwhile, he considered nature itself as only the object of study. He realized the vitality of life by making a close observation of nature and attained the mind of the Heaven and Earth in a detached way. He, as a follower of Neo-Confucianism, enjoyed the land by feeling comfortable with his present status and by being satisfied with himself. He put his Confucian view of universe and world into practice in his life. As a part of his efforts, he named his residence and surrounding natural environments with the polar star and 28 stars, and accordingly they are reconstructed in a system of universe. The Confucian tradition of dongcheon gugok (洞天九曲) starting with Zhu Xi's administration of wuyi jiugu (武夷九曲) was widely prevalent during the Joseon period, but Yeoheon's system of organizing places is original. His sense of naming places reflects his ideas of following his predecessors, comparing natural objects to human emotions, and desiring to live in retirement. Yeoheon understanded the Joseon territory with comparison of the Chinese land. He expressed his knowledge in the form of changing geographical features of a district, appreciating natural beauty, locating towns, and being familiar with a region, and proposing his own climatology and view of the reality. His recognition of the Joseon territory resolves itself into the following several points. He regarded the Joseon territory as one organism, and considered the territory to be composed of ki (氣) as Neo-Confucianists usually do. In addition, he understanded not only natural environments but also towns from a perspective of the fengshui and adopted a comparative methodology in dividing regions. He also applied climatology to analyze persons and customs. He employed the methodology of fengshui from the comprehensive theory of the Yijing. It is because he was influenced by Cho Sik and Jeng Gu. Yeoheon chose dwelling places for people, or gave advice on several places of his hometown relying on his knowledge of fengshui. When it comes to his theory of fengshui, he agreed with the theory of topography with regards to the fengshui of tombs, but criticized the custom of delaying funerals in order to turn fortune in one's favor. In addition, he accepted that it is necessary to complement a town by creating forests around it. We need to pay attention to the fact that Yeoheon's disciples complied several geographical records. It proves that they inherited the tradition of "valuing practical use and governing on behalf of the people" from Cho Sik and Jeong Gu. Yeoheon put a great emphasis on geographical records and encouraged his disciples to compile them. In other words, he emphasized that they, as administrator or intellectual, need to be erudite in the history and custom of a region where they have lived, and have to establish a standard to encourage or warn people in the region while considering the geographical records. His opinion functioned as a guideline for his successors to compile geographical records later. This paper only analyzed several facts with regard to Yeoheon's knowledge of geography and an academic tradition concerning the study of geography. In the future, I shall discuss how his predecessors and successors understanded geography and how the tradition of compiling geographical records was transferred and developed between them. I believe that this study will contribute to establishing the history of geography, which the Joseon Confucianists researched for a long time but we have not paid an enough attention to until now.

• Korean Journal of Poultry Science
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• v.35 no.3
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• pp.291-302
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• 2008

To acquire essentially necessary basic data to establish feeding system by verifying appropriate dietary energy and protein level for the growth of commercial slow growing broiler chicks within the country, two experiments were conducted for 5 weeks. One day old, 1,404 male and female broiler chicks were used for the experiments, and 26 chicks were placed at each pen. The energy level of feed was maintained about 3,000 or 3,100 kg/kcal for whole breeding period of 5 weeks, and protein content was adjusted about 20, 21, and 22% during the first two weeks and the content was adjusted to 18, 19, 20, 21, and 22% from the 3 to 5 weeks old of the experiment. The categories of body weight and feed intake amount were monitored to calculate the productivity and blood sampling was conducted for the analysis at the end of each experiment. Experiment 1:Although the productivity by the ME content difference during $0{\sim}2$ weeks did not have significant difference and the body weight increase by the difference of CP content and feed intake amount did not have much difference, the feed requirement rate was statistically improved in CP 21 and 22% treatment groups compared to the CP 20% group (P<0.05). The feed ME 3,100 kcal/kg treated group during $3{\sim}5$ weeks after starting the experiment revealed to show improved feed requirement rate (P<0.05). Within the period of experiment, the CP 22% treated group resulted to show significant body weight increase compared to the groups treated with low levels of CP (P<0.05) and the feed requirement rate was improved in high CP treated group compared to low CP treated groups, but the feed intake amount did not show significant difference between treated groups. During the experiment period, the body weight increase and feed requirement rate revealed to interact between ME and CP (P<0.05). During the whole experiment period of the 5 weeks, the feed requirement rate was improved in ME 3,100 kcal/kg treated group than the groups treated with ME 3,000 kcal/kg, and the CP (20) 18% treatment groups resulted to show higher values than other treatment groups (P<0.05). Body weight increase was high in CP (22) 22% treated groups than those of CP (21) 21% and (20) 18% treated groups, and the interaction between ME and CP was found at body weight increase and feed requirement rate (P<0.05). Although blood albumin and total cholesterol levels were elevated in ME 3,100 kcal/kg treated group than ME 3,000 kcal/kg treated group, but neutral fat content was reduced (P<0.05). On the other hand, the total cholesterol content was increased in CP (22) 21% treated group than CP (22) 20% and CP (20) 18% treated groups (P<0.05). Experiment 2: The body weight increase in 0-2 weeks was higher in ME 3,100 kcal/kg treated group than ME 3,000 kcal/kg treated group, and it was highly improved in CP 22% treated group than CP 20% treated group by showing the interaction between CP and ME (P<0.05). The significant improvement of feed requirement rate was observed in CP 21% and 22% treated groups compared to CP 20% treated group (P<0.05). The productivity between the growth period from 3 to 5 weeks of age and whole growth period resulted to show no significant difference. Although no difference was observed in blood composition between treated groups, the interaction of ME and CP on cholesterol content was accepted at the range of P<0.05). Therefore, it is considered that the appropriate dietary protein level within feed for the physiology of growing broiler chicks was 22% or more for the first two weeks and protein level of 21% or 20% from 3 to 5 weeks old for the maximization of productivity. Even if the energy level within feed had some partial effects on the productivity, but did not show consistency. So, further experiments needto be conducted by differentiating the energy level.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.171-191
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• 2020

This study focused on digital music, which is the most valuable cultural asset in the modern society and occupies a particularly important position in the flow of the Korean Wave. Digital music was collected based on the "Gaon Chart," a well-established music chart in Korea. Through this, the changes in the ranking of the music that entered the chart for 73 weeks were collected. Afterwards, patterns with similar characteristics were derived through time series cluster analysis. Then, a descriptive analysis was performed on the notable features of each pattern. The research process suggested by this study is as follows. First, in the data collection process, time series data was collected to check the ranking change of digital music. Subsequently, in the data processing stage, the collected data was matched with the rankings over time, and the music title and artist name were processed. Each analysis is then sequentially performed in two stages consisting of exploratory analysis and explanatory analysis. First, the data collection period was limited to the period before 'the music bulk buying phenomenon', a reliability issue related to music ranking in Korea. Specifically, it is 73 weeks starting from December 31, 2017 to January 06, 2018 as the first week, and from May 19, 2019 to May 25, 2019. And the analysis targets were limited to digital music released in Korea. In particular, digital music was collected based on the "Gaon Chart", a well-known music chart in Korea. Unlike private music charts that are being serviced in Korea, Gaon Charts are charts approved by government agencies and have basic reliability. Therefore, it can be considered that it has more public confidence than the ranking information provided by other services. The contents of the collected data are as follows. Data on the period and ranking, the name of the music, the name of the artist, the name of the album, the Gaon index, the production company, and the distribution company were collected for the music that entered the top 100 on the music chart within the collection period. Through data collection, 7,300 music, which were included in the top 100 on the music chart, were identified for a total of 73 weeks. On the other hand, in the case of digital music, since the cases included in the music chart for more than two weeks are frequent, the duplication of music is removed through the pre-processing process. For duplicate music, the number and location of the duplicated music were checked through the duplicate check function, and then deleted to form data for analysis. Through this, a list of 742 unique music for analysis among the 7,300-music data in advance was secured. A total of 742 songs were secured through previous data collection and pre-processing. In addition, a total of 16 patterns were derived through time series cluster analysis on the ranking change. Based on the patterns derived after that, two representative patterns were identified: 'Steady Seller' and 'One-Hit Wonder'. Furthermore, the two patterns were subdivided into five patterns in consideration of the survival period of the music and the music ranking. The important characteristics of each pattern are as follows. First, the artist's superstar effect and bandwagon effect were strong in the one-hit wonder-type pattern. Therefore, when consumers choose a digital music, they are strongly influenced by the superstar effect and the bandwagon effect. Second, through the Steady Seller pattern, we confirmed the music that have been chosen by consumers for a very long time. In addition, we checked the patterns of the most selected music through consumer needs. Contrary to popular belief, the steady seller: mid-term pattern, not the one-hit wonder pattern, received the most choices from consumers. Particularly noteworthy is that the 'Climbing the Chart' phenomenon, which is contrary to the existing pattern, was confirmed through the steady-seller pattern. This study focuses on the change in the ranking of music over time, a field that has been relatively alienated centering on digital music. In addition, a new approach to music research was attempted by subdividing the pattern of ranking change rather than predicting the success and ranking of music.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.