• 지역과문화
• /
• v.8 no.3
• /
• pp.27-48
• /
• 2021

In recent years, China's tourism performing art in a series of new completion of the project, increase the box office of tourism performing arts industry, higher economic income, at the same time led to the formation of brand of tourism performing arts and has a good reputation, with the regional culture, has a certain role in promoting economic development, including Large scenario experience drama is one of the key projects. Large scenario experience drama is a new form of drama that simulates the space design of real environment and enables the audience to have active experience in visual, auditory, smell, taste, touch and other senses with strong interactivity.Large scenario experience drama are adapted from traditional Chinese culture, regional culture and long-passed stories, and combine high technology such as lighting, sound effects, special effects and 3D effects to make the audience's experience more real.As the first Large scenario experience drama in China, 'You Jian Ping-yao' reflects the profound culture of Shanxi with new forms of expression and creative means, in the form of scene experience and make the audience more intuitive feel the 'Shanxi emotion', 'Shanxi sentiment' and 'Shanxi Morality', carry forward the traditional culture at the same time, also passed the Shanxi ancient and great values, strengthened the drama of China's movie village, impetus the development of the tourism industry in Shanxi, drive the Shanxi region of jingjing, gradually formed a complete industrial chain. However, there are also limitations such as improper plot connection and improper tourist management, which can improve the performance effect through more audience interaction and guidance. Therefore, it can be seen that large-scale situational experience dramas play a great role in promoting the dissemination of traditional culture and values, the development of tourism industry, the formation of regional brand characteristics and economic development. Through these, it can be seen that large-scale situational experience plays have enlightenments such as innovative thinking content, gradually forming an industrial chain closed-loop, and broadening publicity channels for the development of live-action performances.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.309-323
• /
• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.1
• /
• pp.165-170
• /
• 2024

'K-contents' is in the spotlight worldwide. As the prefix 'K' became popular, interest in K-food(Korean food) also increased. Various studies on K-contents are being conducted, but research on K-food is still very limited. References and articles about K-food are mainly limited to the overseas expansion, marketing status, and sales of domestic brands, and a few research papers deal with only cases of a specific brand's overseas expansion. This paper aims to analyze how K-food is represented in TV unscripted shows and TV series produced in Korea and what their effects are through empirical works. Among the unscripted shows based on food, they are estimated that the point of competitiveness as K contents deal with foreigners' Korean food experiences. Representative examples here are the way foreigners who visit Korea experience Korean food as part of their Korean culture experience, or the type of temporarily setting up a restaurant overseas to sell Korean food to local people. However, the problem with such shows are that it lacks long-term appeal because it is based on the 'Gukbbong(a slang term for 'extreme nationalism')' sentiment. The exposure of K-food in K-contents creates a tremendous advertising effect. It is judged that the current status and analysis of K-contents based on K-food can help establish the direction of future program production and the identity of K-food.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.1-19
• /
• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.2
• /
• pp.79-93
• /
• 2021

The question of why do you want to start a "start?" Is the most basic step in trying to do something. In other words, previous studies have shown that the degree of confidence in an individual's decision affects the setting of a specific purpose. Based on this, this study aims to provide basic data for deriving the direction of entrepreneurship education in college students by analyzing the effects of goal-oriented behavioral model on college students' self-determination and intention to start a business through the 5 factor model. To achieve the purpose of the study, a self-report questionnaire was conducted from October 01 to November 11, 2019 for university students attending located in Gyeonggi-do, Seoul. A total of 150 questionnaires were distributed, and 125 parts were used for the final analysis, except 25 parts with insincere responses or errors. Data were analyzed using SPSS Win 24, and reliability, validity analysis, frequency analysis, One-way ANOVA and regression analysis were performed, and three-step regression analysis and Sobel verification were performed for mediating effects. The summary of the study is as follows. First, the influence of university students' goal-oriented behavioral model on self-determination showed that attitudes, subjective norms, and perceived behavioral controls had statistically significant positive effects, and positive and negative expectations were statistically significant. Did not affect. Therefore, the higher the attitude, subjective norms, and perceived behavioral control, the higher the university students' self-determination. Second, the influence of college students' goal-oriented behavioral model on the intention to start a business was as follows.). As a result, the higher the perceived behavioral control and positive expectation, the higher the intention to start up. Third, regression model 1 showed that the behavioral control and positive expectation sentiment among the goal-oriented behavioral model had a significant positive influence on the college students' intention to start a business. Affected. Regression model II added the parameters of the 5 factor model, which increased 2.5% of explanatory power than the first regression model. Perceived behavioral control and positive expectations had a statistically significant positive effect, negative expectations had a statistically significant negative effect, and among the 5 factor model, openness had a statistically significant positive (+) Affected. From these results, it can be seen that the Big Five personality factors have a mediating effect on the relationship between goal-oriented behavior model and intention to start up. This study confirmed that the goal-oriented behavioral model of college students is an important variable in implementing self-determination and intention to start a business. In addition, by using his Big 5 personality factors as positive feedback, he has proved to play an important role by identifying the mediation role that can be set, planned and utilized to plan and achieve his life. The result of this study is that college students are interested in the intention of individual start-ups, so they are not freed from difficult employment difficulties. It is intended to provide basic data useful in the age of creation of government.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.113-129
• /
• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.343-355
• /
• 2022

This study investigated the consumption tendency and awareness of paprika in order to expand and promote the consumption of Capsicum annuum L. The research investigated the relationship of preference and loyalty based on emotional response of paprika according to the semantic differential scale. The survey was conducted from January to February 2022 using a random sampling method targeting 155 general people, and a total of 142 questionnaires were analyzed excluding 13 wrong answers. The nine items on the awareness of paprika showed to be consisted of three factors such as 'Food taste', 'Usability', and 'Economics' by factor analysis. Regarding to the awareness of paprika the positive answer that 'I think paprika is good for health' among the nine questions was the highest at 92.3%. In the preference aspect of shape, blocky type had the highest preference for the shape of paprika, followed by mini and conical types in order of preference (p < 0.001). As for color preference, yellow paprika was the most preferred, followed by orange, red, and green, showing statistical significance. The emotional response of paprika by paprika image showed a statistically significant difference in the four colors. The words such as 'bright', 'clean', and 'spirited' appeared as representative emotional vocabulary for paprika. Multiple regression analysis was performed to examine the effect of paprika on the three factors of awareness, preference, and loyalty due to the quality of life. As a result, the higher the paprika preference and quality of life, and the higher the taste and availability factors, the higher the paprika awareness and loyalty. As the variable that has the most influence on the loyalty of the survey respondents, preference was found to have the highest explanatory power at 43%. From these results, it was judged as a very important factor in the survey on the shape and color preference of paprika. Therefore, the recent increase in awareness that paprika is good for health is thought to act as a positive factor in revitalizing the domestic market and increasing consumption of paprika in the future. Also, among the three types of paprika, the yellow blunt type showed the highest preference. Therefore, in order to produce and promote this type of paprika, it is also important to increase the cultivation to suit the purchasing propensity of consumers.

• Cartoon and Animation Studies
• /
• s.37
• /
• pp.197-219
• /
• 2014

, one of the Disney's animations, hit the 10 million audience mark for the first time in the history of animations released in Korea. not only raised the fever with its theme song, 'Let it go', as well as Elsa, Anna, and Olaf's character products but caused sensations in many ways. If so, we need to think about what kind of meaning did create in Korea to be so sensational. This study examines the value that Frozen intended to deliver and the meaning it generated by using Greimas actant model and semiotic square. From the actant model analysis on Anna and Elsa from , it was identified that Anna desired to recover her relationship with Elsa and to take summer back in Arendelle. Her desires can be interpreted as her love toward Elsa and people in Arendelle. Meanwhile, Elsa always desired freedom although she confined herself because of her ability to freeze. In other words, Elsa desired to free herself from her freezing ability by finding out how to control her ability. Such desires of Anna and Elsa were achieved by their actions of true love, and the solution of all the conflicts in was an action of true love. From the semiotic square analysis on the meaning of , it was found out that created past-oriented value with which characters tried to change their abnormal lives of the present into their normal lives of the past. The characters tried to change their present lives where freezing winter comes in the middle of summer, communication between the sisters is cut off, and people try to take advantage of the abnormal state deliberately, into the past when the sisters had a good relationship and the natural season of summer in Arendelle. The past-oriented value that tried to tell us is similar to our reality. In our reality with a lot of unbelievable news and unstable circumstances, we desire to go back to the past when we were filled with affection and hope even though our lives were tough and difficult. This sentiment must have contributed to the huge success of in Korea.

• Journal of Technology Innovation
• /
• v.24 no.1
• /
• pp.113-142
• /
• 2016

T-50 exportation to Indonesia embodied an objective of governmental policy and became a catalyst accelerating the exportation of domestic defense industries. Defense industry exportation is recognized as a new growth engine creating economic interests and it became an important policy of the government. This study will suggest an effective direction for the support policy of the defense industry exportation through analysis on factors behind the success of the T-50 exportation to indonesia in the view of policy network. Policy network theory has its efficacy and workability in analyzing what kind of results are yielded from each policy actor's attributes and their interaction during the execution and establishment of the support policy for the defense exportation. The type of policy network of the T-50 exportation to Indonesia was a policy community. Many governmental ministries, defense industry which is the group of interest, and experts from the research institutes have established the Korea Defense Trade Support Center(KODITS) for accomplishing common policy goal with mutually shared sentiment, and sought for a strategy for the success of the defense industry exportation having official and unofficial meeting centering around the KODITS. Although there were oppositions and conflicts among major actors, though forming a cooperative relationship among majority of the actors, policy-wise decision making for the exportation of the T-50 to Indonesia was efficiently carried out. The cooperative relationship was the key in the success of the T-50 exportation. Considering that the policy community from cooperative mutual interaction is efficient in reaching the goal of the defense industry exportation support policy, this study suggests operating government-wise temporary Task Force(TF) to succeed in big exportation projects such as the T-X exportation to the U.S. In addition, institutional and procedural supplementation such as regular meetings among the head of related governmental ministries and etc. are required in order to enhance the mutually cooperative relationship withing the TF.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.161-177
• /
• 2019

In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.