• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3125-3130
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• 2015

Background: Breast cancer is a common cancer affecting women in Malaysia and the use of complementary/alternative medicine (CAM) has been associated with delays in getting treatment. The aim of the study was to explore the use of CAM and the influencing factors in the Northern region of Peninsular Malaysia. Materials and Methods: This was a cross-sectional descriptive study on a convenience sample of 100 Malaysian breast cancer survivors. Findings: The reported use of CAM among the breast cancer survivors was lower than in other studies but the types of CAM used had a similar pattern with nutrition supplements/vitamins being the most common. The factors that positively influenced the use of complimentary/traditional therapy were income and getting information from television or radio. Survivors with access to internet/blogs appear to have lower odds of using complimentary/traditional therapy compared to the respondents who reported no such access. Conclusions: Information transmitted via television and radio appears to have a positive influence on CAM use by breast cancer patients compared to other information sources and it is important to ensure that such information is accurate and impartial.

• Journal of Information Technology Services
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• v.7 no.3
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• pp.71-85
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• 2008

The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. In this research, we propose a CF-based recommender system for bloggers to find their similar bloggers or preferable virtual community without burdensome search effort. For such a purpose, we apply the "Interest Value" to CF recommender systems. The Interest Value is the quantity value about users' transaction data in virtual community, and can measure the opinion of users accurately. Based on the Interest Value, the neighborhood group is generated, and virtual community list is recommended using the Community Likeness Score (ClS). Our experimental results upon real data of Korean Blog site show that the methodology is capable of dealing with the information overload issue in virtual community space. And Interest Value is proved to have the potential to meet the challenge of recommendation methodologies in virtual community space.

• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.6
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• pp.1060-1070
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• 2017

This study seeks methods to analyze users' perception in fashion designs shown in social media using textmining analysis methods. The research methods selected 'men's stripe shirts' as subjects and collected texts related to the subject mainly from blogs. Texts from 13,648 posts from November 1st, 2015 to October 31st, 2016 were analyzed by applying the LDA algorithm and content analysis. As a result, the wearing status per season and subjects of men's stripe shirts were derived. Across the entire period, the main topics discussed by users to be pattern, customized suits, brands, coordination and purchase information. In terms of seasons, spring time showed the sharing of information on coordinating daily looks or boyfriend looks, and during the winter season the information shared were about shirts suitable for special occasions such as job interviews and stripe shirts that match suits. The study results showed that text-mining analysis is capable of analyzing the context and provide a user-centered index responding to demands newly mentioned by users along with the rapid changes in fashion design trends.

• Journal of Information Science Theory and Practice
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• v.1 no.2
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• pp.36-57
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• 2013

Blogs are readily available sources of opinions and sentiments which allows bloggers to exert a certain level of influence over the blog readers. Previous studies had attempted to analyze blog features to detect influence within the blogosphere, but had not studied in details influence at the blogger-level. Other studies studied bloggers' personalities with regards to their propensity to blog, but did not relate the personalities of bloggers to influence. Bloggers may differ in their way or manner of exerting influence. For example, bloggers could be active participants or just passive shares, or whether they express ideas in a rational or subjective manner, or they are received positively or negatively by the readers. In this paper, we further analyze the engagement style (frequency, scope, originality, and consistency of the blog postings), persuasion style (appeals to reasons or emotions), and persona (degree of compliance) of individual bloggers. Methods used include similarity analysis to detect the sharing-creating aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. While previous studies analyzed influence at blog site level, our model is shown to provide a fine-grained influence analysis that could further differentiate the bloggers' influence style in a blog site.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.57-69
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• 2017

Due to the development of technology even in learning and education area, many studies have begun to make a new attempts to research by using SNS, breaking away from traditional learning methods. However, the limitations of these studies are restricted only to the use of wireless Internet and writing on Web sites. This study aims to conduct a research on English learning activities that utilize various technologies such as Bigdata, Facebook, Social Network Services (SNS) and English applications. In addition, this study looks into how these modern technologies can be integrated in the classrooms and which activities can be applied in the English classroom. This research is to suggest effective English learning methods through a thorough investigation on the effectivity of various technologies based on the Web 2.0 such as Flickr, blogs, MySpace, and online discussion board within the context of the English learning. To verify the effect of the study, the subjects are divided into experimental and control group. The experiment is proceeded with pre- and post-test. The experimental group is designed to verify the effects using SNS tools such as Facebook, Bigdata, and Online Massive Learning. A survey is conducted to determine the preference of utilizing social networking sites and to analyze the effects in class. The result is that the average scores for experimental group have improved more than the average of control group. The comparison of pre and post-test of the experimental group shows that the significance of the higher and median group was statistically significant at the p<0.01.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.373-379
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• 2015

In this paper, we propose an online VQ Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine. Without degrading the performance of the batch VQ Codebook to the existing data, it was able to take advantage of the newly added data by using a triangle inequality which modifying the VQ Codebook progressively show a high degree of accuracy and speed. The result of applying to test data showed that the performance is similar to the batch method.

• Journal of Sasang Constitutional Medicine
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• v.27 no.4
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• pp.388-395
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• 2015

Objectives To grasp the trend of constitution medical industry related to the metabolic disorders by analyzing the web resource.Methods Web search with the search formula ("constitutional" or "spirit") and ("Metabolic" or "diabetes" or "high blood pressure" or "hyperlipidemia" or "obesity") for 20 years (1995.09.10 ~ 2015.09.09.) in the web portal address "Web search with the search formula ("constitutional" or "spirit") and ("Metabolic" or "diabetes" or "high blood pressure" or "hyperlipidemia" or "obesity") for 20 years (1995.09.10 ~ 2015.09.09.) in the web portal address "http://web.search.naver.com".Results In the search area of news, blogs, cafes and knowledge-in, the number of searched pages retrieved by the word "constitution" was about 1.78 million. In the news 9760 cases of "obesity", 4046 cases of "hypertension" and 3253 cases of "diabetes" were searched. In Naver Web search Korean medicine clinics related to "constitution" were 24.3%. If we multiple 25.3% to 1000, the actual number of herbal hospitals, The constitution related to Korean medicine clinics is estimated to be approximately 3160 places. Among metabolic disorders, "Overweight", "Diabetes" and "Hypertension" were most frequently searched.Conclusions Constitutional industry related to metabolic diseases is very actively created on the internet in various areas. Among metabolic diseases, obesity, diabetes, hypertension were found with high frequency.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.581-588
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• 2009

With the development of IT industry and formation of web-based knowledge sharing platform, a variety of railway-related information services on the web have emerged, ranging from personal blogs to dedicated portal sites, as in the other sectors. These services are contributing to advancing railway industry after all. As far as it is concerned with specific areas such as railway sector, the internet users are hardly expected to avail satisfactory results in acquiring customized information from the access, as the information served varies on the intension of the web site operator or relevant agency, and indexing categories and sub-categories is not easy to work out in a straight manner. This study will review on the feasibility of standardizing categories and sub-categories for railway industry information on the web, and present optimum categorization and sub-categorization approach for the most satisfactory results when searched, ultimately aiming at laying a foundation to satisfy the wide spectrum of users' need for railway information.