• Management & Information Systems Review
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• v.35 no.2
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• pp.221-254
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• 2016

With increasing interest of health due to population aging, medical device industry is highlighted as a promising industry. However, Korea medical device industry is not enough market competitiveness compared to global company due to a narrow domestic market and a small company structure. In order to retain the national competitiveness, it is necessary that we have to derive a central technology and its trend. This study has predicted a central technology for medical device industrial using patent network analysis. The central technology is defined as a key technology that is connected to most other technologies and that significantly affects them. For the empirical study, we conducted social network analysis using covariance and correlation coefficient between IPC codes extracted from medical device patents, introduced by Jun(2012). A social network is a social structure of diverse items as well as of human beings. In this study, we set each medical device as a node in an SNA and analyze the Degree values between them. Also, Korea health industrial statistics system are utilized for verification of selected central technology. As a result, we found that the central technology is located on the medical device items, which are listed higher the amount of production. The central technology selected through the proposed methodology will provide a inspiration for establishment of R&D policy.

• The Journal of Bigdata
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• v.1 no.1
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• pp.61-66
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• 2016

With big data analysis, companies use the customized marketing strategy based on customer's information. However, because of the concerns about privacy issue and identity theft, people start erasing their personal information or changing the privacy settings on social network site. Facebook, the most used social networking site, has the feature called 'Likes' which can be used as a tool to predict user's demographic profiles, such as sex and age range. To make accurate analysis model for the study, 'Likes' data has been processed by using Gaussian RBF and nFactors for dimensionality reduction. With random Forest and 5-fold cross-validation, the result shows that sex has 75% and age has 97.85% accuracy rate. From this study, we expect to provide an useful guideline for companies and marketers who are suffering to collect customers' data.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.13-24
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• 2022

SNS has become a very close service to our daily life. As marketing is done through SNS, places often called hot places are created, and users are flocking to these places. However, it is often crowded with a large number of people in a short period of time, resulting in a negative experience for both visitors and service providers. In order to improve this problem, it is necessary to recognize the congestion level, but the method to determine the congestion level in a specific area at an individual level is very limited. Therefore, in this study, we tried to propose a system that can identify the congestion level information and the characteristics of visitors to a specific tourist destination by using the data on the SNS. For this purpose, posting data uploaded by users and image analysis were used, and the performance of the proposed system was verified using the Naver DataLab system. As a result of comparative verification by selecting three places by type of tourist destination, the results calculated in this study and the congestion level provided by DataLab were found to be similar. In particular, this study is meaningful in that it provides a degree of congestion based on real data of users that is not dependent on a specific company or service.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.512-520
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• 2019

This study is to investigate the compulsive use of the social media platform, YouTube. For this purpose, this study built a research model that considered how the personality trait perspective(agreeableness, conscientiousness, neuroticism, extraversion, openness) and the use motivation perspective(utilitarian, hedonic and social use motivations) work together to explain compulsive use of YouTube. The survey method was used for this paper, and data from a total of 165 were used for the analysis. And structural equation model was used to analyze the data. The results of this empirical study is summarized as followings. First, both agreeableness and conscientiousness have a negative effect on compulsive use of YouTube. Second, utilitarian use motivation has a negative effect on compulsive use of YouTube. The results of this study are meaningful in that it not only alerts YouTube users to the risks of compulsive YouTube use, but also helps them to develop self-management strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1800-1808
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• 2022

During the election period, many polling agencies survey and distribute the approval ratings for each candidate. In the past, public opinion was expressed through the Internet, mobile SNS, or community, although in the past, people had no choice but to survey the approval rating by relying on opinion polls. Therefore, if the public opinion expressed on the Internet is understood through natural language analysis, it is possible to determine the candidate's approval rate as accurately as the result of the opinion poll. Therefore, this paper proposes a method of inferring the approval rate of candidates during the election period by synthesizing the political comments of users through internet community posting data. In order to analyze the approval rate in the post, I would like to suggest a method for generating the model that has the highest correlation with the actual opinion poll by using the KoBert, KcBert, and KoELECTRA models.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Journal of Environmental Impact Assessment
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• v.31 no.5
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• pp.310-320
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• 2022

This study aimed to identify the change in the public perception of "bats" after the outbreak of the coronavirus (COVID-19) infection. Text mining and network analysis were conducted for blog posts, the largest social network in Korea. We collected 9,241 Naver blog posts from 2019 to 2020 just before the outbreak of COVID-19 in Korea. The data were analyzed with Python and NetMiner 4.3.2, and the public's perception of bats was examined through the relationship of keywords by period. Findings indicated that the frequency of bat keywords in 2020 increased more than 25 times compared to 2019, and the centrality value increased more than three times. The perception of bats changed before and after the outbreak of the pandemic. Prior to COVID-19, bats were highly recognized as a species of wildlife while in the first half of 2020, they were strongly considered as a threat to human society in relation to infectious diseases and health. In the second half of 2020, it was confirmed that the area of interest in bats expanded as the proportion of ecological and cultural types ofresearch increased. This study seeks to contribute to the expansion and direction of future research in bats by understanding the public's interest in the potential impact of the species as disease hosts post the COVID-19 pandemic.

• Management & Information Systems Review
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• v.37 no.4
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• pp.41-62
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• 2018

As the volume of unstructured data increases through various social media, Internet news articles, and blogs, the importance of text analysis and the studies are increasing. Since text analysis is mostly performed on a specific domain or topic, the importance of constructing and applying a domain-specific dictionary has been increased. The quality of dictionary has a direct impact on the results of the unstructured data analysis and it is much more important since it present a perspective of analysis. In the literature, most studies on text analysis has emphasized the importance of dictionaries to acquire clean and high quality results. However, unfortunately, a rigorous verification of the effects of dictionaries has not been studied, even if it is already known as the most essential factor of text analysis. In this paper, we generate three dictionaries in various ways from 39,800 news articles and analyze and verify the effect each dictionary on the accuracy of document classification by defining the concept of Intrinsic Rate. 1) A batch construction method which is building a dictionary based on the frequency of terms in the entire documents 2) A method of extracting the terms by category and integrating the terms 3) A method of extracting the features according to each category and integrating them. We compared accuracy of three artificial neural network-based document classifiers to evaluate the quality of dictionaries. As a result of the experiment, the accuracy tend to increase when the "Intrinsic Rate" is high and we found the possibility to improve accuracy of document classification by increasing the intrinsic rate of the dictionary.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.95-98
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• 2016

민주주의의 꽃, 제 20 대 국회의원 선거가 막을 내렸다. 지난 선거에서는 방송사뿐만 아니라 정당들도 엄청난 비용 지출과 노력이 소요되었다. 한 예로, 지난 4. 13 총선거 (제 20 대 국회의원)에서 방송 3 사 출구조사 비용으로 약 66 억원 이상이 지출됐다. 그리고 정당에서는 여론조사 비용으로 약 70 억원 이상을 지출했다. 이러한 큰 비용 지출과, 담당자들의 노력을 줄이기 위해 본 논문에서는 텍스트 마이닝과 감정분석을 적용한 후보 당선자 예측 어플리케이션을 제안한다. 첫째, 소셜 그래프 모델을 소개하여 지역 구조를 발견한다. 둘째, 텍스트 마이닝 기법을 이용하여, 후보자 관련 데이터를 가공한다. 셋째, 텍스트 감정 분석을 통해 후보자의 정보를 수치화 한다. 본 논문의 성능과 효율성을 평가하기 위해, 제 20 대 국회의원 선거에 사례연구를 진행하였다. 제안한 방법이 정확도와 수학적 통계 검증을 통해 가치 있는 효율성을 보였다. 선거방송을 위한 후보자 예측 도구의 도입으로 향후 선거(방송)에서의 큰 비용과 노력을 줄이는데 도움을 줄 것이라 기대한다.