• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Journal of the Korean Society of Costume
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• v.62 no.2
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• pp.13-25
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• 2012

The purpose of this research is to study the influence over confidence and Word-Of-Mouth (WOM) effect in the acceptance process of WOM by information characteristics(consensus, vividness, and message neutrality) of individual cosmetic blog, reflecting that it is widely spreading over the public. Online survey for the consumers using such blogs was performed to collect data(N=200), and credibility analysis through Cronbach-${\alpha}$ and Structural Equation Model(SEM) analysis using AMOS 18.0 were performed. The analysis results are as follows; First, vivid information and neutral message increase WOM effect through the improvement of consumers' credibility whereas information consensus doesn't positively influence over credibility. Second, the examination of a moderating effect by type of cosmetics demonstrated that the consumers of basic cosmetics have most confidence in vivid information, and those of color cosmetics do more confidence in neutral message. Therefore, there is a difference in the credibility factor of online WOM depending on the type of cosmetics consumption so that differentiated information provision strategies for cosmetics groups should be established based on it.

• Journal of the Korean Society for information Management
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• v.27 no.1
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• pp.61-77
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• 2010

Blogs are now one of the major information resources on the web. The purpose of this study is to enhance the performance of blog retrieval by means of user assigned tags and trackback information. To this end, retrieval experiments were performed with a dataset of 4,908 blog pages together with their associated trackback URLs. In the experiments, text terms, user tags, and network centrality values based on trackbacks were variously combined as retrieval features. The experimental results showed that employing user tags and network centrality values as retrieval features in addition to text words could improve the performance of blog retrieval.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.116-120
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• 2007

KOSEN(KOrean Scientists and Engineers' Network) started Blog servece to encourage users' knowledge sharing and interaction between users. Since blog opened in April of 2007, 251 blogs have been made. We could collect diverse information from technical knowledge to private hobbies. We could find our blog's limitations and weakness, too. We analyze blog patterns and suggest strategies for service activation in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3070-3075
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• 2010

Through Web 2.0 days, the end users express their opinions and thoughts for blogs and community spaces on the Internet. These opinions and thoughts are used to purchase products, however, users only refer to a few comments not overall opinions. Opinion Analysis System is an opinion search, developed from a natural language search, which analyzes the product's positive or negative evaluations using opinions of products and services on the Internet. In this paper, we suggest a syntactic analysis and inverse processing system that studies and processes 'Positive', 'Negative', 'Neutral' in addition to 'Inverse' information to analyze 'positive' or 'negative' for the core of sentences in Opinion Analysis Service.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.217-224
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• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

• International Journal of Contents
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• v.14 no.3
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• pp.27-31
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• 2018

Social network service (SNS) data related to green tourism were used to estimate preferred tour sites and users' interests. Keywords related with green tour activities were employed to search the SNS data. SNS data were collected from Korean blogs such as Naver and Daum from June $1^{st}$ to August $31^{st}$ between 2015 and 2017 using text-mining solution. During the study period, seven hundred and five posts were analyzed. Associated words that frequently co-occurred with keywords were classified into different categories depending on the nature of associated words. Associated words included swimming pools and camping sites (location); experience and swimming pools (attribute); and water play and culture (culture/leisure). Our data suggest that SNS users with experience of green tourism in Korea exhibited interest in green tourism with swimming pools, camping sites, experience, water play and/or culture rather than particular popular sites. Based on the findings, it is recommended that preferred facilities such as swimming pools should be provided at green tourism sites to meet the users' needs and to facilitate green tourism.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.101-110
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• 2011

According to the increasing use of blogs, a huge number of posts have appeared in a blogosphere. This causes web surfers to face difficulty in finding the quality posts in their search results. As a result, post ranking algorithms are required to help web serfers to effectively search for quality posts. Although there have been various algorithms proposed for web-page ranking, they are not directly applicable to post ranking since posts have their unique features different from those of web pages. In this paper, we propose post ranking algorithms that exploit actions performed by bloggers. We also evaluate the effectiveness of post ranking algorithms by performing extensive experiments using real-world blog data.

• Journal of Information Technology Applications and Management
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• v.18 no.3
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• pp.73-89
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• 2011

More and more employees are implementing the use of emerging Web 2.0 tools such as blogs, wikis, social networks, etc in workplaces. However, their attitudes towards adoption of Web 2.0 tools in workplaces still lack theoretical support. The purpose of this study aims to provide a conceptual examination of the determinants that influence the intention to use Web 2.0 applications in workplaces in Korea. To achieve this objective, this study selected the theory of reasoned action (TRA) as a theoretical basis to explain variation in behavioral intentions. Structural equation modeling was employed to analyze data collected from 269 workers distributed in 5 companies in Korea. In addition, we classified respondents into extroverts and introverts and delineated the different factors for these two types of respondents that affect their intentions to use Web 2.0 tools in workplaces. The findings of this research could provide a theoretical foundation for academics on the validation of technology adoption. This research will also serve as a guideline for service providers in designing the Web 2.0 services.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.113-119
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• 2020

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.