• Korean Journal of Cognitive Science
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• v.26 no.4
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• pp.435-452
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• 2015

Recent researches on Entity Linking(EL) have attempted to disambiguate entities by using a knowledge base to handle the semantic relatedness and up-to-date information. However, EL for tweets using a knowledge base is still unsatisfactory, mainly because the tweet data are mostly composed of short and noisy contexts and real-time issues. The EL system the present work builds up links ambiguous entities to the corresponding entries in a given knowledge base via exploring the news articles and the user history. Using news articles, the system can overcome the problem of Wikipedia coverage (i.e., not handling real-time issues). In addition, given that users usually post tweets related to their particular interests, the current system referring to the user history robustly and effectively works with a small size of tweet data. In this paper, we propose an approach to building an EL system that links ambiguous entities to the corresponding entries in a given knowledge base through the news articles and the user history. We created a dataset of Korean tweets including ambiguous entities randomly selected from the extracted tweets over a seven-day period and evaluated the system using this dataset. We use accuracy index(number of correct answer given by system/number of data set) The experimental results show that our system achieves a accuracy of 67.7% and outperforms the EL methods that exclusively use a knowledge base.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.57-79
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• 2022

COVID-19, which started in December 2019, has had a great impact on our lives in general, including politics, economy, society, and culture, and activities in sports and arts have also been significantly reduced. In the case of sports, sports for all fields in which ordinary citizens participate were particularly affected, and cases of infection in places closely related to people's lives, such as gyms, table tennis, and badminton clubs, also amplified the social fear of the spread of COVID-19. Therefore, in this study, we analyzed news articles related to sports for all at the time when COVID-19 was first spread, and investigated what issues were emerging and being discussed in the sports for all field under the COVID-19 situation. Specifically, we collected news articles dealt with sports for all issues under the COVID-19 situation from Korea's leading portal news sites and identified key sports for all issues by performing topic modeling on these articles. Through the analysis, we found meaningful issues such as COVID-19 outbreak in sports facilities and support for sports activities. In addition, through wordcloud analysis of these major issues, we visually understood the issues and identified the changes in these issues over time.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.59-76
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• 2023

In this study, we tried to find out port safety from various perspectives through news data that can be easily accessed by the general public and domestic academic journal data that reflects the insights of port researchers. Non-negative Matrix Factorization(NMF) based topic modeling was conducted using Python to derive the main topics for each data, and then semantic analysis was conducted for each topic. The news data mainly derived natural and environmental factors among port safety risk factors, and the academic journal data derived security factors, mechanical factors, human factors, environmental factors, and natural factors. Through this, the need for strategies to strengthen the safety of domestic ports, such as strengthening the resilience of port safety, improve safety awareness to broaden the public's view of port safety, and conduct research to develop the port industry environment into a safe and specialized mature port. As a result, this study identified the main factors to be improved and provided basic data to develop into a mature port with a port safety culture.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.2
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• pp.11-20
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• 2020

The goal of this study was to investigate the implementation and related issues of the school space innovation issued by key Korean mass media using text mining. To accomplish this goal, this study collected 519 news articles associated with the school space innovation issued by 54 Korean mass media companies. Based on this data, this study performed the frequency analysis and network analysis regarding the keywords. Based on the findings, the characteristics of school space innovation are summarized as follows: First, school space innovation has progressed in response to future education. Second, users are actively participating in school space innovation. Third, experts are supporting the innovation of school space by establishing a cooperative system. Fourth, the community is actively considering the innovation of school space. Fifth, the main projects of the Ministry of Education and the Provincial Offices of Education are actively conducted in a mix of top-down and bottom-up approaches. The findings of this study will contribute to providing a clear direction for contemporary school space innovation and implications for future research agenda and implementation.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.93-99
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• 2020

Text mining is one of the big data analysis methods that extracts meaningful information from atypical large-scale text data. In this study, text mining was used to monitor citizens' opinions on the policies and systems being implemented. We collected 5,108 newspaper articles and 748 online cafe posts related to 'Gyeonggi Lacal Currency' and performed frequency analysis, TF-IDF analysis, association analysis, and word tree visualization analysis. As a result, many articles related to the purpose of introducing local currency, the benefits provided, and the method of use. However, the contents related to the actual use of local currency were written in the online cafe posts. In order to revitalize local currency, the news was involved in the promotion of local currency as an informant. Online cafe posts consisted of the opinions of citizens who are local currency users. SNS and text mining are expected to effectively activate various policies as well as local currency.

• Smart Media Journal
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• v.11 no.8
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• pp.73-83
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• 2022

In order to support new construction engineering companies with weak information power to successfully advance into the overseas construction market, this study tried to analyze what are the keywords of interest in the overseas construction market and how they differ from Korea. For this purpose, we recently collected 2,473 news article titles and major articles targeting smart cities that are of high interest in Korea and Vietnam. Through network configuration and topic modeling, we examined the connection relationship between the word of interest and the word of interest. In addition, the influence of the word of interest in the network was measured using PageRank centrality. Through this analysis, it was found that there is a high interest in smart city-related construction, cities, and digital in both countries, and the difference in terms of interest between Korea and Vietnam was inferred. Finally, the limitations of this study and additional research directions to complement them are presented.

• Journal of Korean Library and Information Science Society
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• v.38 no.3
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• pp.53-72
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• 2007

The Internet is now an integral part of the everyday lives of a majority of people. They are demanding web sites that offer credible information - Just as much as they want sites that are easy to navigate. But the online reality today is that few Internet users say they can trust the web sites that have products for sale or the sites that offer advice about which products and services to buy. Users want the web sites they visit to provide clear information to allow them to judge the site's credibility. Users want to know who runs the site; how to reach those people; the site's privacy policy; and how the site deals with mistakes. In the eyes of users all sites ate not equal. Users have different credibility standards for different types of sites. For news and information sites users want advertising clearly labeled as advertising. And users want the site to provide a list of the editors responsible for the site's contents, including the editor's email address. For e-commerce sites, user expectations and demands are just about as high as they can be. They say that it is very important that these sites provide specific, accurate information about the site's policies and practices.

• Korean journal of communication and information
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• v.46
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• pp.75-107
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• 2009

Nevertheless reported the same events, news audience have diverse sense of sights and opinions about the events because of the different news frames. This notion was repeatedly evi nnced by several framing studies. This pa wa tried to analyse an interacting relationship between viewer’s level of elaboration likelihood and the effects of the news frames. This pa wa sfrrted with a discussion about the framing effects, then compared them with Elaboration Likelihood Ms notraming effely. And this study conducted an ex waiment selecting indivi ual dispngitions (involvement and cognitive complexity) and message characteristics(number of cues and arguments) as intermediating variables on the message framing effects. This study found out that, the more involvement about the issues the viewers had, the more their thoughts coincided with the issue's frame. On the other hand, when the viewers had low involvement about the issues and cognitive complexity, the framing effects were not found because they processed the messages through the peripheral route. Although the viewers' cognitive complexity was a factor in choosing the central route, but it was not directly connected to the framing effect. Both the number of cues and argument diversity in the messages had positive relationships with the framing effects.