• 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.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.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.55-78
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• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.

• Korean Journal of Community Nutrition
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• v.26 no.3
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• pp.188-199
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• 2021

Objectives: The purpose was to identify the ingredients that are usually surveyed for assessing real prices and to present the demand for such surveys by nutrition teachers and dietitians for ingredients used by school foodservice. Methods: A survey was conducted online from December 2019 to January 2020. The survey questionnaire was distributed to 1,158 nutrition teachers and dietitians from elementary, middle, and high schools nationwide, and 439 (37.9% return rate) of the 1,158 were collected and used for data analysis. Results: The ingredients which were investigated for price realities directly by schools were industrial products in 228 schools (51.8%), fruits in 169 schools (38.4%), and specialty crops in 166 schools (37.7%). Moreover, nutrition teachers and dietitians in elementary, middle, and high schools searched in different ways for the real prices of ingredients. In elementary schools, there was a high demand for price information about grains, vegetables or root and tuber crops, special crops, fruits, eggs, fishes, and organic and locally grown ingredients by the School Foodservice Support Centers. Real price information about meats, industrial products, and pickled processed products were sought from the external specialized institutions. In addition, nutrition teachers and dietitians in middle and high schools wanted to obtain prices of all of the ingredients from the Offices of Education or the District Office of Education. Conclusions: Schools want to efficiently use the time or money spent on research for the real prices of ingredients through reputable organizations or to co-work with other nutrition teachers and dietitians. The results of this study will be useful in understanding the current status of the surveys carried out to determine the real price information for ingredients used by the school foodservice.

• Asia Marketing Journal
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• v.12 no.4
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• pp.55-78
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• 2011

Recently strategic alliance between business firms has become prevalent to overcome increasing competitive threats and to supplement resource limitation of individual firms. As one of allianced sales promotion activities, a new type of discount program, so called "Alliance Card Discount", is introduced with the partnership of credit cards and loyalty cards. The program mainly pursues short-term sales growth by larger discount scheme while spends less through cost share among alliance partners. Thus this program can be regarded as cost efficient discount promotion. But because there is no solid evidence that it can really deliver profitable sales growth, an empirical study for its effects on sales and profit should be conducted. This study has two basic research questions concerning the effects of allianced discount program ; 1)the possibility of sales increase 2) the profitability of the discount driven sales. In F&B industry, sales increase mainly comes from increased guest count. Especially in family restaurants, to increase the number of guests we need to enlarge the size of visitor group (number of visitors for one group) because customers visit by group in a special occasion. And because they pay the bill by group(table), the increase of sales per table is a key measure for sales improvement. The past researches for price & discount sensitivity and reference discount rate explain that price sensitive consumers have narrow reference discount zone and make rational purchase decision. Differently from all time discount scheme of regular sales promotions, the alliance card discount program only provides the right to get discount like discount coupon. And because it is usually once a month opportunity given by the past month usage level, customers tend to perceive alliance card discount as a rare chance to get. So that we can expect customers try to maximize the discount effect when they use the limited discount opportunity. Considering group visiting practice and low visit frequency of family restaurants, the way to maximize discount effect should be the increase the size of visit group. And their sensitivity to discount and rational consumption behavior defer the additional spending for ordering high price menu, even though they get considerable amount of savings from the discount. From the analysis of sales data paid by alliance discount cards for four months, we found the below. 1) The relation between discount rate and number of guest per table is positive : 25% discount results one additional guest 2) The relation between discount rate and the spending per guest is negative. 3) However, total profit amount per table is increased when discount rate is increased. 4) Reward point accumulation & redemption did not show any significant relationship with the increase of number of guests. These results suggest that the allianced discount program substantially contributes to sales increase and profit improvement by increasing the number of guests per table. Though the spending per guest is decreased by discount rate increase, the total amount of profit per table is improved. It seems the incremental profit by increased guest count offsets the profit decrease. Additional intriguing finding is the point reward system does not have any significant impact on the increase of number of guest, even if the point accumulation & redemption of loyalty program are usually regarded as another saving offers by customers. In sum, because it is proved that allianced discount program with credit cards and loyalty cards is effective to both sales drive and profit increase, the alliance card program could be recommended as strategically buyable program.

• Korean Journal of Heritage: History & Science
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• v.45 no.2
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• pp.20-37
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• 2012

After withdrawal of military troops of Chinese Tang dynasty in the 18th year of King Moon-moo's reign(678), the Silla Kingdom had actually unified the Korean peninsula and had divided the territory into 9 states benchmarking the China's local administrations adjustment system. He had established local administrative units by deploying secondary capitals, counties and prefectures in the nine states. The so-called "9 Provinces and 5 Secondary capitals" are what constitutes the local administrations system. The provinces can be compared to current provinces of the Republic of Korea(hereinafter Korea), and secondary capitals to megalopolises. According to a chapter of the Samkuksaki(三?史記) which had recorded the achievements of king Kyoungdeok in December in his 16th year on the throne(757), the local administrative units had amounted to 5 secondary capitals, 117 counties and 293 prefectures. There are still lots of ambiguous points since there have never been any consultation on locations of provinces and secondary capitals' castles, and on structures of cities because the researches for local cities inside the 9 Provinces and 5 Secondary capitals in the Unified Silla Kingdom has been conducted centering on the historic literatures only. The research for restoring structures of cities seen from an archeological perspective are limited to the studies of Taewoo Park("A study on the local cities in the Unified Kingdom Age" 1987) and that of the author("A study on the restoration of planned cities for the Unified Silla Kingdom in terms of the structures and realities of the castles in the 9 Provinces and 5 Secondary capitals" 2009). The Gangneung city of Gangwon province was originally called Haseoryang(河西良) of the Gogureo Kingdom as an ancient nation of Ye(濊). According to "Samkuksaki", it had evolved from Haseoju(河西州) to a secondary capitals in the 8th year of King Seonduk(639). Afterwards, it had been renamed as Myeongju(溟洲) in the 16th year of King Kyoungduk(757), and then several other names were given to it after Goryo dynasty. Taewoo Park claims that it is being defined as a sanctuary remaining in Myoungjudong because of the vestige of bare castle, and this cannot be ascertained due to the on-going urbanization processes. Also, the Kwandong university authority is suggesting an opinion of regarding Myeongju mountain castle located 3 Kms southwest of the center of Gangwon city as commanding post for the pertinent state. The author has restored the pertinent area into a city composed of villages within a lattice framework like Silla Keumkyoung and many other cities. The structure is depicted next. The downtown of Gangneung is situated on a flat terrain at the west bank of Namdaecheon stream flowing southwest to northeast along the inner area of the city. Though there isn't any hill comparatively higher than others in the vicinity, hills are continuously linked east to west along the northern area of the downtown, and the maximum width of flat terrain is about 1 Km and is not so large. Currently, urbanization is being proceeded into the inner portion of Gangneung city, the lands in all directions from the hub of Gangneung station have been readjusted, and thus previous land-zoning program is almost nullified. However, referring to the topographic chart drawn at the time of Japanese colonial rule, it can be validated that land-zoning program to accord the lattice framework with the length of its one side equaling to 190m leaves its vestige about 0.8Km northwest to southeast and about 1.7Km northeast to southwest of the vicinity of Okcheondong, Imdangdong, Geumhakdong, Myeongjudong, and etcetera which comprize the hub of the downtown. The land-zoning vestige within the lattice framework, compared to other cases related with the '9 states and 5 secondary capitals', is very much likely to be that of the Unified Silla Kingdom. That the length of a side of a lattice framework is 190m as opposed to that of Silla Geumkyoung and other cities with their 140m or 160m long sides is a single survey item in the future. The baseline direction for zoning the lands is tilting approximately 37.5 degrees west of northwest to southeast axis in accordance with the topographic features. It seems that this phenomenon takes place because of the direction of Namdaecheon and the geographic constraints of the hills in the north. Reviewing minimally, a rectangular size of zoned land by 4 Pangs(坊) on the northwest to southeast side multiplied by 7 Pangs(坊) on the northeast to southwest side had been restored within a lattice framework. Otherwise, considering the extent of expansion of the existing zoned lands in the lattice framework and one more Pang(坊) being added to each side, it is likely that the size could have been with 5 Pangs(坊) on the northwest to southeast side multiplied by 8 Pangs(坊) on the northeast to southwest side(950 M on the northwest to southeast side multiplied by 1,520m on the northeast to southwest side). The overall shape is rectangle, but land-zoning programs reminiscent of rebuilt roads(red phoenix road) like Jang-an castle(長安城) of Chinese Tang dynasty or Pyoungseong castle(平城城) in Japan is not to be validated. There are some historic items among the roof tiles and earthen wares excavated at local administrative office sites or Gangneung's town castle in Joseon dynasty inside the area assumed to be containing municipal vestiges even though archeological survey for the vestige of Myeongju has not been made yet, and these items deserve dating back to the Unified Silla Kingdom age. Also, all of the construction sites at local administrative authorities of the Joseon dynasty are showing large degrees of slant in the azimuth. This is a circumstantial evidence indicating the fact that the inherited land-zoning programs to be seen in Gangneung in terms of the lattice framework had ever existed in the past. Also, the author does not decline that Myeongju mountain castle had once been the commanding post when reviewing the roof tiles at the edge of eaves in this stronghold. The ancient municipal castles in the Korean peninsula are composed of castles on the flat terrain as well as hilly areas and the cluster of strongholds like Myounghwal, Namhan, Seohyoung mountain castles built around municipal castle of Geumkyoung based on a lattice framework program. Considering that mountain castles are spread in the vicinity of municipal vestiges in other cities other than the 9 states and 5 secondary capitals, it is estimated that Myeongju was assuming the function of commanding post incorporating cities on the flat terrain and castles on the hills.