• Informatization Policy
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• v.24 no.4
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• pp.3-16
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• 2017

With the development of social media services in the era of Web 2.0, the public opinion formation site has been partially shifted from the traditional mass media to social media. This phenomenon is continuing to expand, and public opinions on government polices created and shared on social media are attracting more attention. It is particularly important to grasp public opinions in policy formulation because setting up educational policies involves a variety of stakeholders and conflicts. The purpose of this study is to explore public opinions about education-related policies through an empirical analysis of social media documents on education policies using opinion mining techniques. For this purpose, we collected the education policy-related documents by keyword, which were produced by users through the social media service, tokenized and extracted sentimental qualities of the documents, and scored the qualities using sentiment dictionaries to find out public preferences for specific education policies. As a result, a lot of negative public opinions were found regarding the smart education policies that use the keywords of digital textbooks and e-learning; while the software education policies using coding education and computer thinking as the keywords had more positive opinions. In addition, the general policies having the keywords of free school terms and creative personality education showed more negative public opinions. As much as 20% of the documents were unable to extract sentiments from, signifying that there are still a certain share of blog posts or tweets that do not reflect the writers' opinions.

• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.53-63
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• 2018

The traditional mass media function of conveying information and forming public opinion has rapidly changed into an environment in which information and opinions are shared through social media with the development of ICT technology, and such social media further strengthens its influence. In other words, it has been confirmed that the influence of the public opinion through the production and sharing of public opinion on political, social and economic changes is increasing, and this change is already in use on the political campaign. In addition, efforts to grasp and reflect the opinions of the public by utilizing social media are being actively carried out not only in the political area but also in the public area. The purpose of this study is to explore the possibility of using social media based public opinion in educational policy. We collected media data, analyzed the main topic and probability of occurrence of each topic, and topic trends. As a result, we were able to catch the main interest of the public(the 'Domestic Computer Education Time' accounted for 43.99%, and 'Prime Project Selection' topics was 36.81% and 'Artificial Intelligence Program' topics was 7.94%). In addition, we could get a suggestion that flexible policies should be established according to the timing of the curriculum and the subject of the policy even if the category of the policy is same.

• 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.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.1-9
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• 2016

In accordance with the increased political and social utilization of social media, demands on online trend analysis and monitoring technologies based on social bigdata are also increasing rapidly. In this paper, we define 'risk' as issues which have probability of turn to negative public opinion among big social issues and classify their types in details. To define risk types, we conduct a complete survey on news documents and analyzed characteristics according to issue domains. We also investigate cross-medias analysis to find out how different public media and personalized social media. At the result, we define 58 risk types for 6 domains and developed automatic classification model based on machine learning algorithm. Based on empirical experiments, we prove the possibility of automatic detection for social issue risk in social media.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.225-226
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• 2019

오피니언 마이닝은 온라인 문서의 감성을 추출하여 분석하는 기법이다. 별도의 여론조사 없이 감성을 분석 가능하므로, 최근 활발한 연구 분야이다. 그러나 소셜미디어에는 신조어 등이 많이 포함되어 있어 기존 감성분석 시스템으로는 정확한 분석이 어려울 뿐만 아니라, 복합적인 감성에 대한 분석을 내리기에 불리하다. 이에 본 연구에서는 직관적인 감성모델을 제안하고 SNS에서 주목받는 다양한 신조어를 수용한 감성단어사전을 구축한 후, 이를 적용하여 소셜미디어에 나타나는 복합적인 감성을 분석하는 감성분석시스템을 설계한다.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.309-318
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• 2017

This study is for analyzing the tone, the frame and the characteristics of political news in social media. Social news media is not same as old media in sharing news freely by SNS like tweeter, facebook and reporting, editing by anyone using SNS with various opinions. With Content analysis, sampling 419 cases from 'Wikitree' by the keyword, 'presidential election', all the full text analysed each how is social media making public opinion differently and which frame is using in. As the result, the social media has different tone, frame, and characteristic due to the reported figure, type of report, information source, attitude to the government, specifically shows a lack of in-depth report and distinct soft-journalism just same as old media's. Because the tone of social news media is not probable, specific but improbable, vague, using the irrational, strategic and episodic frame mainly.

• Knowledge Management Research
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• v.22 no.4
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• pp.217-235
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• 2021

As 49% of the world's population uses social media platforms, communication and content sharing within social media are becoming more active than ever. In this environmental base, the one-person media market grew rapidly and formed public opinion, creating a new trend called sell-sumer. This study defined new types of influencers by product category by analyzing the subject concentration of the commercial/non-commercial keywords of influencers and the impact of the ratio of commercial postings on sales. It is hoped that influencers working within social media will be helpful to new sales strategies that are transformed into sell-sumers. The method of this study classifies influencers' commercial/non-commercial posts using Python, performs text mining using KoNLPy, and calculates similarity between FastText-based words. As a result, it has been confirmed that the higher the keyword theme concentration of the influencer's commercial posting, the higher the sales. In addition, it was confirmed through the cluster analysis that the influencer types for each product category were classified into four types and that there was a significant difference between groups according to sales. In other words, the implications of this study may suggest empirical solutions of social media sales strategies for influencers working on social media and marketers who want to use them as marketing tools.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.541-548
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• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.771-774
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• 2012

세계적으로 온라인 평판 분석 서비스의 개발 및 SNS 이슈 분석과 영향도 평가 등의 서비스 이용추세가 증가하고 있다. 현재 SNS나 소셜미디어등 온라인을 통해 고객들의 서비스에 대한 정성적인 평가 및 요구사항이 실시간으로 표현되고 있으며, 서비스에 대한 부정적인 여론이 확산되기 전에 능동적으로 대응할 수 있는 시스템의 필요성이 기업들에게 시급히 요구되고 있다. 이러한 시스템 개발을 위해서 선행되어야 할 기술개발 요건으로 B2C 산업의 특성에 기반한 다양한 분석 주제를 설정하고, 이에 맞는 다양한 동일 산업군에 대한 시스템 적용을 위한 분류 표준화가 필수이다. 본 연구에서는 이를 수행하기 위해서 대표 산업군을 선정하여 기존 업무 분류 체계를 기반으로 한 온라인상의 고객 피드백 분류 및 표준화 수립에 대한 방법을 제안한다.

• 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.