• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

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

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

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• MISULJARYO - National Museum of Korea Art Journal
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• v.96
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• pp.123-154
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• 2019

Ancestral Burial Ground on the Inwangsan Mountain is a ten-panel folding screen with images and postscripts. Commissioned by Bak Gyeong-bin (dates unknown), this screen was painted by Jo Jung-muk (1820-after 1894) in 1868. The postscripts were written by Hong Seon-ju (dates unknown). The National Museum of Korea restored this painting, which had been housed in the museum on separate sheets, to its original folding screen format. The museum also opened the screen to the public for the first time at the special exhibition Through the Eyes of Joseon Painters: Real Scenery Landscapes of Korea held from July 23 to September 22, 2019. Ancestral Burial Ground on the Inwangsan Mountain depicts real scenery on the western slopes of Inwangsan Mountain spanning present-day Hongje-dong and Hongeun-dong in Seodaemun-gu, Seoul. In the distance, the Bukhansan Mountain ridges are illustrated. The painting also bears place names, including Inwangsan Mountain, Chumohyeon Hill, Hongjewon Inn, Samgaksan Mountain, Daenammun Gate, and Mireukdang Hall. The names and depictions of these places show similarities to those found on late Joseon maps. Jo Jung-muk is thought to have studied the geographical information marked on maps so as to illustrate a broad landscape in this painting. Field trips to the real scenery depicted in the painting have revealed that Jo exaggerated or omitted natural features and blended and arranged them into a row for the purposes of the horizontal picture plane. Jo Jung-muk was a painter proficient at drawing conventional landscapes in the style of the Southern School of Chinese painting. Details in Ancestral Burial Ground on the Inwangsan Mountain reflect the painting style of the School of Four Wangs. Jo also applied a more decorative style to some areas. The nineteenth-century court painters of the Dohwaseo(Royal Bureau of Painting), including Jo, employed such decorative painting styles by drawing houses based on painting manuals, applying dots formed like sprinkled black pepper to depict mounds of earth and illustrating flowers by dotted thick pigment. Moreover, Ancestral Burial Ground on the Inwangsan Mountain shows the individualistic style of Jeong Seon(1676~1759) in the rocks drawn with sweeping brushstrokes in dark ink, the massiveness of the mountain terrain, and the pine trees simply depicted using horizontal brushstrokes. Jo Jung-muk is presumed to have borrowed the authority and styles of Jeong Seon, who was well-known for his real scenery landscapes of Inwangsan Mountain. Nonetheless, the painting lacks an spontaneous sense of space and fails in conveying an impression of actual sites. Additionally, the excessively grand screen does not allow Jo Jung-muk to fully express his own style. In Ancestral Burial Ground on the Inwangsan Mountain, the texts of the postscripts nicely correspond to the images depicted. Their contents can be divided into six parts: (1) the occupant of the tomb and the reason for its relocation; (2) the location and geomancy of the tomb; (3) memorial services held at the tomb and mysterious responses received during the memorial services; (4) cooperation among villagers to manage the tomb; (5) the filial piety of Bak Gyeong-bin, who commissioned the painting and guarded the tomb; and (6) significance of the postscripts. The second part in particular is faithfully depicted in the painting since it can easily be visualized. According to the fifth part revealing the motive for the production of the painting, the commissioner Bak Gyeongbin was satisfied with the painting, stating that "it appears impeccable and is just as if the tomb were newly built." The composition of the natural features in a row as if explaining each one lacks painterly beauty, but it does succeed in providing information on the geomantic topography of the gravesite. A fair number of the existing depictions of gravesites are woodblock prints of family gravesites produced after the eighteenth century. Most of these are included in genealogical records and anthologies. According to sixteenth- and seventeenth-century historical records, hanging scrolls of family gravesites served as objects of worship. Bowing in front of these paintings was considered a substitute ritual when descendants could not physically be present to maintain their parents' or other ancestors' tombs. Han Hyo-won (1468-1534) and Jo Sil-gul (1591-1658) commissioned the production of family burial ground paintings and asked distinguished figures of the time to write a preface for the paintings, thus showing off their filial piety. Such examples are considered precedents for Ancestral Burial Ground on the Inwangsan Mountain. Hermitage of the Recluse Seokjeong in a private collection and Old Villa in Hwagae County at the National Museum of Korea are not paintings of family gravesites. However, they serve as references for seventeenth-century paintings depicting family gravesites in that they are hanging scrolls in the style of the paintings of literary gatherings and they illustrate geomancy. As an object of worship, Ancestral Burial Ground on the Inwangsan Mountain recalls a portrait. As indicated in the postscripts, the painting made Bak Gyeong-bin "feel like hearing his father's cough and seeing his attitudes and behaviors with my eyes." The fable of Xu Xiaosu, who gazed at the portrait of his father day and night, is reflected in this gravesite painting evoking a deceased parent. It is still unclear why Bak Gyeong-bin commissioned Ancestral Burial Ground on the Inwangsan Mountain to be produced as a real scenery landscape in the folding screen format rather than a hanging scroll or woodblock print, the conventional formats for a family gravesite paintings. In the nineteenth century, commoners came to produce numerous folding screens for use during the four rites of coming of age, marriage, burial, and ancestral rituals. However, they did not always use the screens in accordance with the nature of these rites. In the Ancestral Burial Ground on the Inwangsan Mountain, the real scenery landscape appears to have been emphasized more than the image of the gravesite in order to allow the screen to be applied during different rituals or for use to decorate space. The burial mound, which should be the essence of Ancestral Burial Ground on the Inwangsan Mountain, might have been obscured in order to hide its violation of the prohibition on the construction of tombs on the four mountains around the capital. At the western foot of Inwangsan Mountain, which was illustrated in this painting, the construction of tombs was forbidden. In 1832, a tomb discovered illegally built on the forbidden area was immediately dug up and the related people were severely punished. This indicates that the prohibition was effective until the mid-nineteenth century. The postscripts on the Ancestral Burial Ground on the Inwangsan Mountain document in detail Bak Gyeong-bin's efforts to obtain the land as a burial site. The help and connivance of villagers were necessary to use the burial site, probably because constructing tombs within the prohibited area was a burden on the family and villagers. Seokpajeong Pavilion by Yi Han-cheol (1808~1880), currently housed at the Los Angeles County Museum of Art, is another real scenery landscape in the format of a folding screen that is contemporaneous and comparable with Ancestral Burial Ground on the Inwangsan Mountain. In 1861 when Seokpajeong Pavilion was created, both Yi Han-cheol and Jo Jung-muk participated in the production of a portrait of King Cheoljong. Thus, it is highly probable that Jo Jung-muk may have observed the painting process of Yi's Seokpajeong Pavilion. A few years later, when Jo Jungmuk was commissioned to produce Ancestral Burial Ground on the Inwangsan Mountain, his experience with the impressive real scenery landscape of the Seokpajeong Pavilion screen could have been reflected in his work. The difference in the painting style between these two paintings is presumed to be a result of the tastes and purposes of the commissioners. Since Ancestral Burial Ground on the Inwangsan Mountain contains the multilayered structure of a real scenery landscape and family gravesite, it seems to have been perceived in myriad different ways depending on the viewer's level of knowledge, closeness to the commissioner, or viewing time. In the postscripts to the painting, the name and nickname of the tomb occupant as well as the place of his surname are not recorded. He is simply referred to as "Mister Bak." Biographical information about the commissioner Bak Gyeong-bin is also unavailable. However, given that his family did not enter government service, he is thought to have been a person of low standing who could not become a member of the ruling elite despite financial wherewithal. Moreover, it is hard to perceive Hong Seon-ju, who wrote the postscripts, as a member of the nobility. He might have been a low-level administrative official who belonged to the Gyeongajeon, as documented in the Seungjeongwon ilgi (Daily Records of Royal Secretariat of the Joseon Dynasty). Bak Gyeong-bin is presumed to have moved the tomb of his father to a propitious site and commissioned Ancestral Burial Ground on the Inwangsan Mountain to stress his filial piety, a conservative value, out of his desire to enter the upper class. However, Ancestral Burial Ground on the Inwangsan Mountain failed to live up to its original purpose and ended up as a contradictory image due to its multiple applications and the concern over the exposure of the violation of the prohibition on the construction of tombs on the prohibited area. Forty-seven years after its production, this screen became a part of the collection at the Royal Yi Household Museum with each panel being separated. This suggests that Bak Gyeong-bin's dream of bringing fortune and raising his family's social status by selecting a propitious gravesite did not come true.