• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.37-48
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• 2022

With development of Digital Technology, social issues are communicated through digital-based platform such as SNS and form public opinion. This study attempted to analyze big data from Twitter, a world-renowned social network service, and find out the public opinion. After collecting Twitter data based on 14 keywords for 1 year in 2021, analyzed the term-frequency and relationship among keyword documents with pearson correlation coefficient using Data-mining Technology. Furthermore, the 6 main topics that on the center of information security field in 2021 were derived through topic modeling using the LDA(Latent Dirichlet Allocation) technique. These results are expected to be used as basic data especially finding key agenda when establishing strategies for the next step related industries or establishing government policies.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1719-1724
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• 2013

Big data which requires a different approach from existing data processing methods, is unstructured data with a variety of features. The features mean the volume of data, the rate of change of the data, the data with a variety of features. Tweets of twitter in only Korea are more than 5 millions per day. So much cheaper data storage and analysis system due to the increasing demand for information, the value of research is increasing. In this paper, the technology required by the deformation characteristics of the data elements as a technology priority-based word-based recommendation algorithm is proposed.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.269-286
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• 2013

The advent of Web 2.0 and social media is taking a leading role of emerging big data. At the same time, however, informational dysfunction such as infringement of one's rights and violation of social order has been increasing sharply. This study, therefore, aims at defining malicious usage, identifying malicious feature, and devising an automated method for classifying them. In particular, the rule-based experiment reveals statistically significant performance enhancement.

• The Journal of Bigdata
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• v.2 no.2
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• pp.57-66
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• 2017

Companies has been investing enormous resources in promotion as the market keeps changing rapidly. Therefore, there are growing needs to measure the impact of a promotion on revenue growth. To investigate the effect of promotion in franchise food service industry, this study empirically analyzed text data from Twitter, one of the dominant social network services. Our findings show that a gap between promotions, promotion duration, and season have a significant influence on a volume of twitter buzz, which represents a promotion effect in our study. Next, we tried to analyze the reason why those factors were related to the promotion effect. Finally, we suggested promotion strategies related to each influential factor depending on types of business in food service industry.

• Convergence Security Journal
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• v.14 no.4
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• pp.33-40
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• 2014

Since 2006, social networking services such as Facebook, Twitter, and Blog user increasing very rapidly. Furthermore demand of Book Retrieval Service using smartphone on social network service environment are increasing too. This service can to easy and share information for search book and data in several university. However, the current edition of the social services in the country to provide security services do not have the right. Therefore, we suggest a social book Retrieval service in social network environment that can support user authentication and partial filter search method on smartphone. our proposed system can to provide more speed responsiveness, effective display result on smartphone and security service.

• Fashion & Textile Research Journal
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• v.22 no.4
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• pp.481-494
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• 2020

This study examined consumer perceptions and consumer responses of Halal cosmetics and compared them with vegan cosmetics, which is a term similarly used. Twitter API of Python 3.7 was used to collect the keywords '#halalcosmetics' and '#vegancosmetics'. First, the main perception of consumers on Halal cosmetics focused on the original concept, image, expected efficacy, and factors to consider before purchase, religious keywords, labels and packaging for Halal cosmetics. Second, the main consumer perception of vegan cosmetics was the product concept, expected efficacy, factors to consider before purchase, related vegan industry, image, and vegan cosmetic components. Third, the consumer perceptions of Halal cosmetics and vegan cosmetics were similar in multiple ways, and both concepts included the Cruelty-free concept. Fourth, consumer satisfaction factors included cosmetics color, brand's consumer service, efficacy, smell, packaging design, reasonable price, effects, and formulation of cosmetics as well as satisfaction with Halal certification, and satisfaction of Vegan consumers. Consumer dissatisfaction factors included smell, flavor, delay in shipping, dissatisfaction with formulation, discrepancy between actual color and computer screen, concern and distrust about the use of prohibited ingredients for Halal products. This study examined consumer perceptions and reactions to Halal and vegan cosmetics to create basic knowledge for niche markets that are emerging as an ethical beauty consumption trend.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.731-738
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• 2013

We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.