• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.33-55
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• 2022

This study analyzed the comments and the replies on internet news related to the presidential election in order to verify whether online discussions are properly conducted. According to Habermas' public sphere theory, discussions is an effort among participants to reach a social consensus through the deliberations that are based on open communications. We propose that if such discussions properly take place through the act of writing in the Internet space, the comments and the replies will show a certain difference in terms of the structure and the content. To validate, this study analyzed more than 40,000 comments collected from Daum News portal site in Korea. The topic of the related news was the presidential election, because it is a topic of which people are highly interested in and that comments are actively running. The result of the t-test and topic modeling result show that all the hypotheses were supported thus we conclude that online discussions properly took places. This study also showed that online comments are not chaotic remarks that relieve people's stresses, but rather an outcome of the deliberation processes moving towards a social consensus.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.167-173
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• 2023

As research has been published to predict future data using regression analysis or artificial intelligence as a method of analyzing economic indicators. In this study, we designed a system that predicts prospective futures prices using artificial intelligence that utilizes topic probability data obtained from past news articles using topic modeling. Topic probability distribution data for each news article were obtained using the Latent Dirichlet Allocation (LDA) method that can extract the topic of a document from past news articles via unsupervised learning. Further, the topic probability distribution data were used as the input for a Long Short-Term Memory (LSTM) network, a derivative of Recurrent Neural Networks (RNN) in artificial intelligence, in order to predict prospective futures prices. The method proposed in this study was able to predict the trend of futures prices. Later, this method will also be able to predict the trend of prices for derivative products like options. However, because statistical errors occurred for certain data; further research is required to improve accuracy.

• Nutrition Research and Practice
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• v.15 no.sup1
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• pp.110-121
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• 2021

BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.30-45
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• 2022

This study explored the issues of industrial mathematics in online news articles and online forums in Korea and the US by using text mining and compared the results. Text data about industrial mathematics were collected from news articles of Naver, a major portal site, and postings and replies on Clien as resources of Korea, and from news articles by the New York Times and CNN as well as postings and replies on Reddit as resources of the US. Structural topic modeling analyses were performed, the major results of which were as follows. First, news articles in Korea mainly dealt with the necessity of industrial mathematics and government support. On the contrary, the news articles in the US focused more on various fields where industrial mathematics fields were utilized. Second, in Korea, the same number of issues with different topics were discussed in news articles and online forums, whereas in the US more issues were covered in news articles than in online forums. It was suggested academic implications for researchers and practical implications for the government for settling industrial mathematics in Korea.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.945-963
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• 2022

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information-both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.1-14
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• 2021

Purpose: The article deals with the proposition that consumers' fashion consumption behavior will still follow the consumption behavior of fast fashion, despite recognizing the importance of slow fashion. Research design, data and methodology: The research model to verify this proposition is topic modelling with big data including unstructured textual data. we combined 5,506 news articles posted on Naver news search platform during the 2003-2019 period about fast fashion and slow fashion, high-frequency words have been derived, and topics have been found using LDA model. Based on these, we examined consumers' perception and consumption behavior on slow fashion through the analysis of Topic Network. Results: (1) Looking at the status of annual article collection, consumers' interest in slow fashion mainly began in 2005 and showed a steady increase up to 2019. (2) Term Frequency analysis showed that the keywords for slow fashion are the lowest, with consumers' consumption patterns continuing around 'brand.' (3) Each topic's weight in articles showed that 'social value' - which includes slow fashion - ranked sixth among the 9 topics, low linkage with other topics. (4) Lastly, 'brand' and 'fashion trend' were key topics, and the topic 'social value' accounted for a low proportion. Conclusion: Slow fashion was not a considerable factor of consumption behavior. Consumption patterns in fashion sector are still dominated by general consumption patterns centered on brands and fast fashion.

• Journal of Fashion Business
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• v.22 no.2
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• pp.134-149
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• 2018

The number of children wearing make-up is increasing. "Children's cosmetics" is not a legal term though it is commonly used. The purpose of this study is to analyze discussions on children's cosmetics based on news articles found on the internet. This study also identifies what products are being distributed as children's cosmetics. Keyword searches were conducted using internet portal sites. Information was extracted from news articles and Best Item 100 for children's cosmetics. The results of analyzing news articles and Best Item 100 lists are as follows : 1. There were two main discussion topics in news articles. The first topic was related to marketing(the branding and trends of children's cosmetics). The other topic was about government regulations(side effects, harmful ingredients, control, regulations, attention, proper product usage, product categorization, and the overall safety of children's cosmetics). By 2014, many articles had covered government control and regulation. However, since 2017, news articles have focused on the product categorization and the concern for overall safety has dramatically increased. 2. Three different product categories have appeared in the Best Item 100; they are cosmetics, toys, and other products. In market, consumers recognized children's cosmetics as cosmetics and also as toys. Between 2017 and 2018's Best Item, other products are dramatically down, color cosmetics and single cosmetics are on the rise, and the purchase of domestic products has increased.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.457-466
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• 2020

The purpose of this study is to find out what the main agenda of social formation is and how it changes through the media by utilizing the news big data of COVID-19 which is spreading recently, and to suggest the direction of future reporting. In order to achieve the purpose of the research, 47,816 cases of news big data reported from December 31, 2019 to March 11, 2020 were divided into four periods based on the fourth stage of the crisis warning for infectious diseases, and a total of 20 topics were derived. Based on the results of the Topic Modeling analysis, this study proposed the following. First, it is necessary to refrain from provocative expressions such as "anxiety" and "fear" and use neutral and objective reporting terms. Second, more in-depth and contextual news production is required, breaking away from simple event news production. Third, it is necessary to prepare detailed crisis communication manuals for each situation related to infectious diseases. Fourth, we need reports that focus on citizens-led efforts to overcome the crisis. This research has the academic significance that it is the first paper to analyze news big data on COVID-19 using the Topic Modeling Analysis method, and the policy significance that can be used as the basis for developing national crisis communication policy.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.396-399
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• 2012

Topic detection is a problem of discovering the topics of online publishing documents. For topic detection, it is important to extract correct topic words and to show the topical words easily to understand. We consider a topic tree-based approach to more effectively and more briefly show the result of topic detection for online text documents. In this paper, to achieve the topic tree-based topic detection, we propose a new term weighting method, called CTF-CDF-IDF, which is simple yet effective. Moreover, we have modified a conventional clustering method, which we call incremental k-medoids algorithm. Our experimental results with Reuters-21578 and Google news collections show that the proposed method is very useful for topic detection.