• Design Convergence Study
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• v.15 no.2
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• pp.347-368
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• 2016

Recently, researches for the word of mouth(WOM) imply that consumers use WOM informations of products in their purchase process. This study suggests methods using opinion mining and visualization to understand consumers' opinion of each goods and each markets. For this study we conduct research that includes developing domain ontology based on reviews confined to "movie" category because people who want to have watching movie refer other's movie reviews recently, and it is analyzed by opinion mining and visualization. It has differences comparing other researches as conducting attribution classification of evaluation factors and comprising verbal dictionary about evaluation factors when we conduct ontology process for analyzing. We want to prove through the result if research method will be valid. Results derived from this study can be largely divided into three. First, This research explains methods of developing domain ontology using keyword extraction and topic modeling. Second, We visualize reviews of each movie to understand overall audiences' opinion about specific movies. Third, We find clusters that consist of products which evaluated similar assessments in accordance with the evaluation results for the product. Case study of this research largely shows three clusters containing 130 movies that are used according to audiences'opinion.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.5
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• pp.281-290
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• 2019

The study aims to analyze teachers' perception of the "personality" area, which can be subjective in the in-depth interview process of selecting gifted children and is easily shunned due to its weak immediate effect. To this end, First, when asked about their difficulties as gifted teachers, many of them answered "professionalism and workload" and cited personality as the most important area to address in-depth interviews in selecting gifted students. It also recognized that personality interviews are necessary for the most basic virtues of education and social contribution, and cited cooperation, consideration, and concession as the sub-components to be dealt with in the personality interview. It was necessary to check whether each student's capabilities were evaluated in a variety of ways in an in-depth interview of the teacher's observing and recommending system. And it needed to be supplemented by in-depth observations such as the development of a valid question, camp or debate in the evaluation of the personality area. In order to reflect the needs of the education field, it will be necessary to supplement the personality interview in the gifted children's selection. And there is also a need to continue to study how to guide the personality education of already selected gifted children.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.37-50
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• 2017

This study was performed to understand Malaysian Muslims' awareness, attitudes and purchasing behaviour on ginseng (G) and red ginseng (RG) products. A survey of 200 Muslims residing in Malaysia was conducted on awareness, eating experience, preferences, cognitive efficiency of G and RG products, purchase behaviors and satisfaction through a online-survey methodology. Results shows that 50 % and 40% of the participants aware the G and RG products. In particular, awareness amongst female or married consumers is relatively high. Health promotion is the major reasons to consume eat G and RG products in this group of participants. However, the most frequently consumed type of G products was ginseng coffee, candies and chocolates, in their 40s and 50s or married consumers. Participants are also aware of the efficacy claims of these products with regard to improvement of fatigue, immunity and hypertension. While Malaysian Muslim consumers are satisfied with the health claims, convenience to purchase and tastes and aroma, they are dissatisfied with packaging specifications, price. Participants would intend to recommend G and RG products to relatives (82.6%), and are willing to buy them in the future (83.5%). Conclusively, there must be a clear interest and demands of Halal-certified G and RG products among Malaysian Muslims and it is deemed to need of strategic product development and marketing to enhance awareness of G and RG products in the future.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.29-34
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• 2016

Nowadays, real-time devices that provide health care for a companion animal is being developed by IoT technology and its demand such as smart puppy tag is increasing. However, it is difficult for IoT devices of companion animals to process complex nature due to miniaturized hardware and constructive nature. There is a clear limit to custom advanced features like health care implementation. This paper designs an integrated platform with statistical analysis which makes it possible to customized services such as feed production, pharmaceutical production, and health care for each companion animal. Middleware that collects sensor information, customer's spending pattern and information from Social Network Service is also designed by making use of IoT devices which companion animals wear. Furthermore, the paper designed data analyzer which analyzes and refines data from collected information that can be applied to personalized services.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.17-22
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• 2015

High School and College Bridge Program for high school students to learn about specialty training through Mentors with high tech equipment before college admissions has been established. In this study we further developed the previous research experience in 2013 to mentoring program. According to this program, we developed the team learning program and then conducted analytic study on the second year achievement. They became mentees and undergraduate students or graduate course student were mentors. High school students learned how to solve problems by themselves under the mentoring education. The mentees had higher satisfaction in lecture and interest increasing factors at first part of the program. In second part, they showed more satisfaction in new knowledges and recommendation factors. The relationship and intimacy have grown through interaction between mentors and mentees during the team learning. The high school and college bridge program would have significant meaning to develope a customized program for high school students through continuous study.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.117-123
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• 2013

The development of the latest emotional recognition and multimedia technology has changed the traditional broadcasting system. The previous broadcasting system, which was operated by the terrestrial broadcasters, is now transformed to the viewer-centered and bidirectional broadcasting through the convergence of internet, mobile and smart TV. In this paper, smart-phone application for estimating human emotion(sadness, anger, depression) has been developed and emerged with smart TV, thereby we can present broadcasting service for enhancing the sense of common humanity among people of same group. If there is friend in the depression, we can bring comfort to him by inviting one for TV program what I watch and having a honest talk with facial avatar or emoticon. The proposed emotional broadcasting service inter-working with smart-phone application can give feeling of belonging and happiness to the people suffering from the blues, and it can prevent him from attempting suicide. In addition, smart-phone based emotional broadcasting service can be expended to program recommendation service customized to user's emotion, emotional LED lighting service to maximize the sense of reality and home shopping service taking advantage of the mood of customer.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.614-618
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• 2008

As the multimedia data increase a lot with the rapid development of the Internet, an efficient retrieval technique focusing on individual users is required based on the analyses of such data. However, user modeling services provided by recent web sites have the limitation of text-based page configurations and recommendation retrieval. In this paper, we construct the user preference model with a Bayesian network to apply the user modeling to video retrieval, and suggest a method which utilizes probability reasoning. To do this, context information is defined in a real office environment and the video scripts acquired from established cameras and annotated the context information manually are used. Personal information of the user, obtained from user input, is adopted for the evidence value of the constructed Bayesian Network, and user preference is inferred. The probability value, which is produced from the result of Bayesian Network reasoning, is used for retrieval, making the system return the retrieval result suitable for each user's preference. The usability test indicates that the satisfaction level of the selected results based on the proposed model is higher than general retrieval method.

• Journal of The Korean Association of Information Education
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• v.20 no.2
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• pp.189-196
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• 2016

The PATROL instructional model only uses digital textbooks. PATROL is an acronym for Planning, Action, Tracking, Recommending, Ordering, and Leading. Teachers have a difficult time using current digital textbooks to determine how much time students spend using course materials. This is because current digital textbooks can only show the content of paper textbooks and display additional multimedia materials. In this study, digital textbook functions were designed based on the PATROL model in order to analyze students' learning situations, diagnose problems, and offer solutions. Digital textbook are based on learning analytics named SEE-PAD. SEE-PAD is composed of the following: Social network analysis; Evaluation and assEssment analysis; Predictive analysis; Adaptive learning analysis; and the analysis Dashboard. I drew and showed the use case and sequence diagrams of SEE-PAD to help design digital textbook functions.