• Journal of Digital Convergence
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• v.18 no.9
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• pp.17-27
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• 2020

News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.85-94
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• 2013

With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users' participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users' judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users' based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users' output better outcome can be achieved then ordinary clustering algorithms.

• Journal of Society of Preventive Korean Medicine
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• v.15 no.2
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• pp.131-143
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• 2011

Objective : The aims of this study are to investigate the trend of internet journalism about the toxicity and safety of the herbal medicine, and to suggest the regulatory solution of the issue. Method : In this study, we had searched the internet news article published from 2001 to 2011 in the five major portal sites-NAVER, DAUM, Nate, Google Korean, and Yahoo Korean. The search terms were 'herbal medicine', 'adverse event', 'toxicity'. If the articles described the same event in the same form and tone, the articles were considered overlapping. The overlapped articles were excluded. The articles were categorized by the form and tone. The form categories were straight news, interpretative story, editorial, interview, and the tone categories are the positive, the negative, and the neutral. The regulations were searched about the negative issue. Result : Total 56 articles were reviewed. There were 19 positive articles, 29 negative articles, 8 neutral articles. Most negative issues have the proper regulations, but insufficient measures for the adverse event reporting system. Conclusion : The herbal medicine specified adverse event reporting system is essential.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.63-71
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• 2011

The current Web portal sites provide significant keywords with high popularity or importance; specifically, user-friendly services such as tag clouds and associated word search are provided. However, in general, since news articles are classified only with their date and categories, it is not easy for users to find other articles related to some articles while reading news articles classified with categories. And the conventional associated keyword service has not satisfied users sufficiently because it depends only upon user queries. This paper proposes a way of searching news articles by utilizing the keywords tightly associated with users' queries. Basically, the proposed method discovers a set of keyword association patterns by using the association rule mining technique that extracts association patterns for keywords by focusing upon sentences containing some keywords. The method enables users to navigate the space of associated keywords hidden in large news articles.

• Journal of Preventive Medicine and Public Health
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• v.53 no.2
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• pp.126-134
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• 2020

Objectives: Cancer diagnoses have a tremendous impact on individuals and communities, drawing intense public concern. The objective of the current research was to examine news coverage and content related to cancer-related issues in Korean newspapers. Methods: Primarily using the database system of the Korea Press Foundation, we conducted a content analysis of 2806 articles from 9 Korean daily newspapers during a recent 3-year period from 2015 to 2017. Thematic categories, the types of articles, attitudes and tone, and the number of sources in each article were coded and classified. Results: Many news articles dealt with a diverse range of themes related to cancer, including general healthcare information, the latest research and development, specific medical institutions and personnel, and technology and products, which jointly accounted for 74.8% of all articles. Those thematic categories differed markedly in terms of article type, tone, and the number of cited sources. News articles provided extensive information about healthcare resources, and many articles seemed to contain advertising content. However, the content related to complex social issues such as National Health Insurance did not include enough information for the reader to contextualize the issues properly or present the issues systematically. Conclusions: It can be assumed that the media exert differential influence on individuals through news coverage. Within the present reporting framework, the availability and usefulness of information are likely to depend solely on individuals' capabilities, such as financial and health literacy; this dependency has a negative impact on knowledge gaps and health inequities.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.111-119
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• 2023

This study aimed to analyze the changes in news articles related to 'Elderly Living Alone' by comparing Big Data-based news articles related to 'Elderly Living Alone' reported before and after the outbreak of COVID-19. For this, 2018 to 2019 were selected before the outbreak of COVID-19, and 2020 to 2021 were selected after the outbreak, and news articles related to 'Elderly Living Alone' were collected and analyzed using BIGKinds. The main results are as follows. First, the number of related articles decreased after the outbreak of COVID-19 compared to before. Second, there was no significant difference in the analysis of related words. Third, in the relationship diagram analysis, 'Executives' before the outbreak of COVID-19 and 'Corona 19' after that showed the most weight. This study is expected to be used as basic data in preparing improvement plans for national policies and systems in the context of the spread of infectious diseases in relation to 'Elderly Living Alone'.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.8
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• pp.251-258
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• 2020

A training dataset for text summarization consists of pairs of a document and its summary. As conventional approaches to building text summarization dataset are human labor intensive, it is not easy to construct large datasets for text summarization. A collection of news articles is one of the most popular resources for text summarization because it is easily accessible, large-scale and high-quality text. From social media news services, we can collect not only headlines and subheads of news articles but also summary descriptions that human editors write about the news articles. Approximately 425,000 pairs of news articles and their summaries are collected from social media. We implemented an automatic extractive summarizer and trained it on the dataset. The performance of the summarizer is compared with unsupervised models. The summarizer achieved better results than unsupervised models in terms of ROUGE score.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.352-359
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• 2021

The purpose of this study is to design and build fake news discrimination systems and methods using fact-checking guidelines. In other words, the main content of this study is the system for identifying fake news using Artificial Intelligence -based Fact-checking guidelines. Specifically planned guidelines are needed to determine fake news that is prevalent these days, and the purpose of these guidelines is fact-checking. Identifying fake news immediately after seeing a huge amount of news is inefficient in handling and ineffective in handling. For this reason, we would like to design a fake news identification system using the fact-checking guidelines to create guidelines based on pattern analysis against fake news and real news data. The model will monitor the fact-checking guideline model modeled to determine the Fact-checking target within the news article and news articles shared on social networking service sites. Through this, the model is reflected in the fact-checking guideline model by analyzing news monitoring devices that select suspicious news articles based on their user responses. The core of this research model is a fake news identification device that determines the authenticity of this suspected news article. So, we propose news article identification methods with fact-checking guideline on Artificial Intelligence & Bigdata. This study will help news subscribers determine news that is unclear in its authenticity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1751-1754
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• 2022

The news articles collected with keyword "non face-to-face" were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-face-to-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.89-91
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• 2017

A currency exchange is the rate to exchange currencies between different countries and the one of important factors to measure the economic size or status of a country. The currency exchange is affected by various economic or social events and changed dynamically. However, since too many economic and social factors affect the exchange rate and the leverage rate of each factor is so floating, it is difficult to define clearly the relationships between the exchange rate and the specific factor. In this paper, we analyze the data pattern for the exchange rate and news articles. To do this, we counts the frequencies of words presented in the news articles during specific periods and compare the frequencies with the margins of exchange rates.