• Journal of Korean Home Economics Education Association
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• v.23 no.4
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• pp.143-157
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• 2011

This study aims to identify family strength and career maturity, and to explore the difference of career maturity according to family strength, targeting high school students as subjects. A total of 1,000 copies of the questionnaire were distributed and 858 copies were used for the final analysis. The major results are summarized as follows: In respect to general tendency of family strength and career maturity, the score of family respect was the highest, 3.59, followed by 3.56 of gratitude and affection, 3.54 of problem shooting ability, economic stability, faithfulness to role, emotional bond, positive communication and goal sharing. The score of emotional bond with society was the lowest, 2.90. The score of family strength was 3.36, which was the intermediate level. As sub-factors of career maturity, the score of relevancy was 3.40, and that of tendency was 3.17, followed by that of independence and compromise. The score of determination was the lowest, 2,75. A total score of career maturity was 3.05. Family strength was marked highly by the girls students studying humanities, and religious ones. Those whose parents have high education and no-divorce background also showed high scores. High school students with higher economic levels and long-married parents showed higher scores than high school students with poor economy levels and single parents. Students with parents who have professional jobs also marked high scores. Career maturity degree as a background variable was significantly higher in the following cases: In terms of religious background, students whose mother are buddhists or atheists marked high scores. High scores were marked by those whose parents have higher education and no-divorce background. High school students from middle class and over and long-married parents showed higher career maturity degree than high school students with poor economy levels and single parents. Students with parents who have professional jobs also marked high career maturity degree. While career maturity degree, which is subjected to family strength, showed very different results when analysed with 5 sub-variables. As family strength resulted in positive effects to career maturity degree, higher family strength showed higher career maturity degree. Especially, in the areas of relevance and tendency of career maturity degree, the effects of positiveness was clearly high.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Land and Housing Review
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• v.3 no.4
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• pp.369-378
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• 2012

In France, there are many forms of organizations based on the intercommunal solidarity for city development and management. The purpose of the collaboration among Communes is to achieve high quality and well-equipped service-delivery system through co-operation of public services needed grand finances : water supply and sewage system and waste disposal system etc. The cooperation among French Communes and its effects, even though these were owing to the existing French local administration system, continued throughout regional co-management and social co-development process. This study suggested some characteristics and implications of the collaborative-style French new-town development and management organizations focused on the EPA, SAN and CA. First, the role of developmental corporation like EPA and its collaborative structure of decision-making are meaningful, because in these ways many related Communes could share a goal of new town development. Second, the way of new town corporation (SAN) is important in the sense of enabling the Communes to collaborate with each others while maintaining autonomy, so those are not simply state-directed objects, which was very difficult in the former French local administration system. Finally, transforming to CA (Communautes d'agglomeration:city community), EPA as an intercommunal corporation is possible to extend its purpose to the domain of regional planning including new town and periphery areas and change its position to a subject which can practice Commune's sustainable development according to stages of city's development and maturity. The most important implication of this study on urban development in Korea is that administrative consultative council or association among local governments and related authorities need to be established and effectively operate because multi-stakeholders could share a goal of urban development and management through that.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.1
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• pp.108-119
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• 2015

Recently, the ways in which individual communities own and manage parks have been both discussed and realized in America and England. Some benefits of these asset transfers are that local governments can reduce the financial cost of management as well as improve the service of the parks. In addition, communities can develop these parks as unique assets. Ultimately, this is a new understanding of parks as community commons. This study examines the policy of park asset transfers to communities in England. These transfers, which involve reallocating land and building management and/or ownership from the public sector to a community group, are part of a policy agenda known as "Big Society", which aims to create a "small government" within a "big society". The agenda is pursued by both the English Conservative and Unionist Party governments. Eight case studies of community park asset transfers in England were examined in this study, under three categories-transfer process, partnership among stakeholders, and financial structure-and synthesized along three issues-financial contribution, level of public transparency, and closeness of the relationship between park and community. In some cases, new community groups were created specifically to receive park assets, while in other communities, existing groups became the park trustees. For most parks, community groups raise park maintenance funding through diverse methods; however, these groups are often not entirely financially independent from local government. Thus, many park trustees have already created, or are planning to create, other assets from which parks can benefit. Second, some efforts for public transparency include trusts that are registered as charities, of which their public nature is admitted officially. These trusts resolve important decisions through boards of trustees, in an effort to promote income-generating business while not excluding users. Ultimately, a close relationship between park and community empowers the community to participate in managing and maintaining the park; in turn, the park's capacities are improved. Current struggles include the many limits involved in communities accepting ownership and management of a park, and a lack of local government experience regarding public-private management and maintenance of a public asset. This study, however, details interesting policy implications for Korean community involvement as well as diverse financial methods to facilitate park management.

• Journal of Distribution Science
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• v.8 no.1
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• pp.21-33
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• 2010

The ICT(information & communication technology) led to a dramatic change of floral distribution service, a phase of competition between wholesales and retail stores, and distribution channels in floral industry. It was expected that a role of the intermediaries in this industry would have reduced due to the improvement of transaction process by ICT. However, the ICT made to overcome a regional limit of the floral retail distribution service leading to an increase in sales and enlargement of the stores. And even it made possible to bring out another type of intermediaries such as private associations. This case study focuses on what kinds of efforts the floral wholesale distributors have made to enable a distribution process more smoothly between the wholesale distributors and retail stores through the information system, and what the failure factors in adopting the information system have been. This paper is also to examine how the wholesale distributors have changed themselves to gain dominant positions in distribution channels. As a result of the study, it was found that the intermediaries mostly failed in successfully achieving the distribution channel innovation through the information system because of several main reasons. FLOMARKET Inc. tried to innovate a distribution channel to obtain high quality goods through consolidating a wholesale distribution market in that segregated both floral joint market from free markets. after implementing the information system with consideration of the failure factors, FLOMARKET Inc. was able to minimize goods in stock and make a major purchase of various goods. In addition, it made a possible pre-ordering process and an exact calculation of purchasing goods so they could provide their products with market price in real time, which helped for the company to gain credits from their customers. Also, FLOMARKET Inc. established the information system which well suited to its business stage in order to deal with a rapidly changing distribution environment. It's so obvious that the transaction processing system of FLOMARKET Inc. definitely helped to share information among traders more seamlessly and smoothly in realtime, standardize goods, and make a transaction process clearer. Besides, the transaction information helped the wholesale distributors and retail stores to make more strategic decisions in their business because through the system they enabled to gather the marketing intelligence information more easily and convenient. If we understand that the floral distribution market is characterized by the low IT- based industry, it's worth to examine a case study proving that the information system actually increases the productivity of the transaction process in the floral industry.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.