• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.81-91
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• 2019

Recently, social network service (SNS) is actively used by public. Among them, Twitter has a lot of tweets including sentiment and it is convenient to collect data through open Aplication Programming Interface (API). In this paper, we analyze social issues and suggest the possibility of using them in marketing through sentimental information of users. In this paper, we collect twitter text about social issues and classify as positive or negative by sentiment classifier to provide qualitative analysis. We provide a quantitative analysis by analyzing the correlation between the number of like and retweet of each tweet. As a result of the qualitative analysis, we suggest solutions to attract the interest of the public or consumers. As a result of the quantitative analysis, we conclude that the positive tweet should be brief to attract the users' attention on the Twitter. As future work, we will continue to analyze various social issues.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.51-58
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• 2018

As the amount of users and data of NS explosively increased, research based on SNS Big data became active. In social mining, Latent Dirichlet Allocation(LDA), which is a typical topic model technique, is used to identify the similarity of each text from non-classified large-volume SNS text big data and to extract trends therefrom. However, LDA has the limitation that it is difficult to deduce a high-level topic due to the semantic sparsity of non-frequent word occurrence in the short sentence data. The BTM study improved the limitations of this LDA through a combination of two words. However, BTM also has a limitation that it is impossible to calculate the weight considering the relation with each subject because it is influenced more by the high frequency word among the combined words. In this paper, we propose a technique to improve the accuracy of existing BTM by reflecting semantic relation between words.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.680-682
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• 2015

최근 스마트폰의 보급으로 소셜 네트워크 서비스를 이용하는 사용자들이 급증하였다. 그 중 트위터는 정보의 빠른 전파력과 확산성으로 인해 현실에서 발생한 이벤트를 탐지하는 도구로 활용하는 것이 가능하다. 따라서 트위터 사용자 개개인을 하나의 센서로 가정하고 그들이 작성한 트윗 텍스트를 분석한다면 이벤트 탐지의 도구로써 활용할 수 있다. 이와 관련된 연구들은 이벤트 발생 위치를 추적하기 위해 GPS좌표를 이용하지만 트위터 사용자들이 위치정보 공개에 회의적인 점을 감안하면 명확한 한계점으로 제시될 수 있다. 이에 본 논문에서는 트위터에서 제공하는 위치정보를 이용하지 않고, 트윗 텍스트에서 위치정보를 추적하는 방법을 제시하였다. 트윗 텍스트에서 키워드간의 관계를 고려하여 이벤트의 사실여부를 결정하였으며, 실험을 통해 기존 매체들보다 빠른 탐지를 보임으로써 제안된 시스템의 필요성을 보였다.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.765-767
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• 2011

IT 와 비즈니스가 융합화 되고 정보가 디지털화 됨에 따라 그에 대한 저장매체도 점점 더 다양해지고 있다. 그 중 이동성이 편리하고 휴대하기 간편한 스마트폰을 활용하여 개인 정보를 주고 받고 이를 이용한 비즈니스가 현재 활발히 진행되고 있다. 이러한 소셜 네트워크 서비스 이용이 급격히 증가함에 따라 개인 정보 보안에 대한 중요성은 점점 더 강조 되고 있는 실정이다. 본 연구에서 제안하는 디지털 포렌식 분석 방법을 이용하면 스마트폰에서 지원하는 서비스 형태에 따라 텍스트, 이미지, 동영상 등의 개인 정보를 수집 및 분석이 가능하다. 또한 디지털 포렌식의 관점에 따라 스마트폰 에서 사용되고 있는 애플리케이션의 로그 정보를 수집 및 분석함으로써 스마트폰의 저장 장치에 남겨진 기록들을 훼손 없이 그대로 보존시키고 디지털 증거 자료로 활용이 가능해 사이버 범죄에 대한 신속한 해결이 가능하다.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.57-66
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• 2015

Considerable research efforts are being directed towards analyzing unstructured data such as text files and log files using commercial and noncommercial analytical tools. In particular, researchers are trying to extract meaningful knowledge through text mining in not only business but also many other areas such as politics, economics, and cultural studies. For instance, several studies have examined national pending issues by analyzing large volumes of text on various social issues. However, it is difficult to provide successful information services that can identify R&D documents on specific national pending issues. While users may specify certain keywords relating to national pending issues, they usually fail to retrieve appropriate R&D information primarily due to discrepancies between these terms and the corresponding terms actually used in the R&D documents. Thus, we need an intermediate logic to overcome these discrepancies, also to identify and package appropriate R&D information on specific national pending issues. To address this requirement, three methodologies are proposed in this study-a hybrid methodology for extracting and integrating keywords pertaining to national pending issues, a methodology for packaging R&D information that corresponds to national pending issues, and a methodology for constructing an associative issue network based on relevant R&D information. Data analysis techniques such as text mining, social network analysis, and association rules mining are utilized for establishing these methodologies. As the experiment result, the keyword enhancement rate by the proposed integration methodology reveals to be about 42.8%. For the second objective, three key analyses were conducted and a number of association rules between national pending issue keywords and R&D keywords were derived. The experiment regarding to the third objective, which is issue clustering based on R&D keywords is still in progress and expected to give tangible results in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.6
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• pp.16-24
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• 2020

The purpose of this study is to analyze the usage behavior of Seoullo 7017, the first public garden in Korea, to understand the usage status by analyzing blogs, and to present usage behavior and improvement plans for Seoullo 7017. From June 2017 to May 2020, after Seoullo 7017 was open to citizens, character data containing 'Seoullo 7017' in the title and contents of NAVER and·DAUM blogs were converted to text mining and socialization, a Big Data technique. The analysis was conducted using social network analysis. The summary of the research results is as follows. First of all, the ratio of men and women searching for Seoullo 7017 online is similar, and the regions that searched most are in the order of Seoul and Gyeonggi, and those in their 40s and 50s were the most interested. In other words, it can be seen that there is a lack of interest in regions other than Seoul and Gyeonggi and among those in their 10s, 20s, and 30s. The main behaviors of Seoullo 7017 are' night view' and 'walking', and the factors that affect culture and art are elements related to culture and art. If various programs and festivals are opened and actively promoted, the main behavior will be more varied. On the other hand, the main behavior that the users of Seoullo 7017 want is 'sit', which is a static behavior, but the physical conditions are not sufficient for the behavior to occur. Therefore, facilities that can cause sitting behavior, such as shades and benches must be improved to meet the needs of visitors. The peculiarity of the change in the behavior of Seoullo 7017 is that it is recognized as a good place to travel alone and a good place to walk alone as a public multi-use facility and group activities are restricted due to COVID-19. Accordingly, in a situation like the COVD-19 pandemic, more diverse behaviors can be derived in facilities where people can take a walk, etc., and the increase of various attractions and the satisfaction of users can be increased. Seoullo 7017, as Korea's first public pedestrian area, was created for urban regeneration and the efficient use of urban resources in areas beyond the meaning of public spaces and is a place with various values such as history, nature, welfare, culture, and tourism. However, as a result of the use behavior analysis, various behaviors did not occur in Seoullo 7017 as expected, and elements that hinder those major behaviors were derived. Based on these research results, it is necessary to understand the usage behavior of Seoullo 7017 and to establish a plan for spatial system and facility improvement, so that Seoullo 7017 can be an important place for urban residents and a driving force to revitalize the city.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.136-140
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• 2009

최근 기존 블로그와 다른 마이크로 블로그의 한 종류로 트위터가 인터넷 상에서 화두로 대두되고 있다. 트위터는 기존 블로그나 미니홈피의 여러 가지 기능을 간소화하고 짧은 내용의 텍스트만을 올릴 수 있는 마이크로 블로그이다. 그런 이유로 트위터는 단순함과 즉시성이라는 고유의 특성을 가지고 일반적인 인터넷 이용자들에게 급속하게 알려지고 있다. 이러한 트위터를 분석하면 다양한 주제에 대해서 인터넷상의 대중들의 생각과 의견들을 알 수 있는 창구가 될 수 있다. 또한 다른 언어권 국가들의 트위터와 비교하면 양 국가간의 문화적 차이를 알 수 있다. 본 논문에서는 한국어 및 영어권 이용자들의 트위터 상의 메시지를 주제별, 목적별 등으로 분석하였다. 그 결과, 한국에서는 트위터 이용을 개인적인 생각을 적는 일기장으로 많이 사용되지만, 영어권 에서는 그 외에도 보도 자료나 광고등 여러 가지 목적으로 사용되고 있다는 것을 알 수 있다.

• Korean Educational Research Journal
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• v.43 no.1
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• pp.1-27
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• 2022

As the COVID-19 virus became prevalent worldwide, non-face-to-face contact was implemented in various ways, and the education system also began to draw much attention due to rapid non-face-to-face contact. The purpose of this study is to analyze the direction of non-face-to-face education in line with the continuously changing educational environment to date. In this study, data were visualized using Textom and Ucinet6 analysis tool programs to collect social network big data with various opinions. As a result of the study, keywords related to "COVID-19" were dominant, and keywords with high frequency such as "article" and "news" existed. As a result of the analysis, various issues related to non-face-to-face education, such as network failures and security issues, were identified. After the analysis, the direction of the non-face-to-face education system was studied according to the growth of the education market and changes in the educational environment. In addition, there is a need to strengthen security and feedback on teaching methods in non-face-to-face education analyzed using big data.