• Journal of Digital Convergence
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• v.15 no.7
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• pp.383-391
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• 2017

The purpose of this study was to examine users' attitude toward likability, understanding level, reliability, satisfaction, and continuous usage intention depending on a difference in a type between card news and straight news. A questionnaire survey was conducted targeting 232 people. As a result of the research, compared to the straight news, the card news was easy for a user to understand, was strong even in a desire to use continuously. Second, a factor of users' attitude toward news was having influence upon the mutually positive(+) direction. Likability, understanding level and reliability had an effect on satisfaction. The satisfaction had an impact again on continuous usage intention. The intensity of this impact was varied, respectively, in card news and straight news. The influential level upon satisfaction in card news was strong in order of likability, understanding level and reliability. The influence in the straight news was strong in order of reliability, likability and understanding level. The outcome of this study will become empirical data in proceeding with seeking a method available for strengthening the function of offering information in news through increasing delivery and impact in information with producing news chosen by consumers.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.54-60
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• 2011

The study is content-based video retrieval system based on the news video domain as researching for the video data processing method for searching the multimedia information. For the implementation of effective system, We retrieval meaning information and special information using the knowing knowledge about formation and structure of the video data. These are possible to retrieval searching by articles fast and accurately by indexing contents the users want to search. The news domain used in experiment of our system is the KBS news on the air nowadays and precision and recall is used to evaluate the experiment and performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.378-384
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• 2023

In this study, we explore social awareness, interest, and acceptance of generative AI, including chatGPT, which has revolutionized web search, 30 years after web search was released. For this purpose, we performed a machine learning-based topic modeling analysis based on Korean news big data collected from November 30, 2022, when chatGPT was released, to August 31, 2023. As a result of our research, we have identified seven topics related to chatGPT and generative AI; (1)growth of the high-performance hardware market, (2)service contents using generative AI, (3)technology development competition, (4)human resource development, (5)instructions for use, (6)revitalizing the domestic ecosystem, (7)expectations and concerns. We also explored monthly frequency changes in topics to explore social interest related to chatGPT and Generative AI. Based on our exploration results, we discussed the high social interest and issues regarding generative AI. We expect that the results of this study can be used as a precursor to research that analyzes and predicts the diffusion of innovation in generative AI.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1489-1497
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• 2018

The importance of comments is constantly growing as a participation of individual in Online News that being invigorated. The 'Best Comments', which strongly related by major participants are recognized as a primary public opinion, and obtains the power. Thus this study is aimed to analyze the characteristics of the 'Best Comments' by utilizing the data of comments on Online News. For this study, a possible element that may reveal the difference between 'general' comments and 'best' comments were set up, digitalized the data, and examined the difference between 'general' and 'best' comments. This study is expected to provide a clue for the problematic issues, such as 'online comment rigged scandal' in recent; also as a basic data that subjected by the individual, academic society, government, and etc.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.492-501
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• 2019

To use the system safely in cyberspace, you need to use a security solution that is appropriate for your situation. In order to strengthen cyber security, it is necessary to accurately understand the flow of security from past to present and to prepare for various future threats. In this study, information security words of security/hacking news of Naver News which is reliable by using text mining were collected and analyzed. First, we checked the number of security news articles for the past seven years and analyzed the trends. Second, after confirming the security/hacking word rankings, we identified major concerns each year. Third, we analyzed the word of each security solution to see which security group is interested. Fourth, after separating the title and the body of the security news, security related words were extracted and analyzed. The fifth confirms trends and trends by detailed security solutions. Lastly, annual revenue and security word frequencies were analyzed. Through this big data news analysis, we will conduct an overall awareness survey on security solutions and analyze many unstructured data to analyze current market trends and provide information that can predict the future.

• 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'.