• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.3
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• pp.167-173
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• 2017

With increased interest in the fourth industrial revolution represented by artificial intelligence, it has been very active to utilize bigdata and machine learning techniques in almost areas of society. Also, such activities have been realized by development of forecasting systems in various applications. Especially in the movie industry, there have been numerous attempts to predict whether they would be success or not. In the past, most of studies considered only the static factors in the process of prediction, but recently, several efforts are tried to utilize realtime social bigdata produced in SNS. In this paper, we propose the prediction technique utilizing various feedback information such as news articles, blogs and reviews as well as static factors of movies. Additionally, we also experimentally evaluate whether the proposed technique could precisely forecast their revenue targeting on the relatively successful movies.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.65-75
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• 2014

The main focus of this paper is to analyze trend of event informations in a variety of mass media by graphical visualization in axis of the time and location. Especially, continuity analysis based on user-generated social media can reflect the social impact of a certain event according to change time and location and their directional changes. To reveal the characteristics of continuous events, we survey the data set collected from news articles and tweets during two years. Based on case studies on 'disease' and 'leisure', we verify the effectiveness and usefulness of our proposed method. Even though some events occurred during same period, we showed directional changes which have high-impact in social media referred user interest's, compared with fact-based continuous visualization results.

• Korean Journal of Health Education and Promotion
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• v.28 no.5
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• pp.131-143
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• 2011

Objectives: This study is to develop a web-based prevention program of alcohol-related problem among university students and to examine its efficacy in changing drinking behaviors and reducing harms associated with alcohol use. Methods: An hour-length program was developed containing news paper articles, pictures, stories and questionnaires in a multimedia format such as flash, animation, and text. Survey data were analyzed to examine program efficacy from a convenient sample of 1,080 freshmen enrolled in a participating university. Results: Participation in the program has resulted in statistically significant changes in drinking behavior between pretest and follow-up, including reduction of frequency of drinking, heavy drinking, and intoxication, typical amount of drinking per occasion, and subjective norm of heavy drinking. Repeated ANOVA revealed significant increases in knowledge on alcohol related facts, including recommended drinking unit by WHO with substantial level of effect size and in making negative alcohol expectancy with very low effect size. Further studies are guaranteed with randomized controlled trial for the program effectiveness. Conclusions: A web-based prevention program of alcohol-related problems in this study was efficacious in changing drinking behavior, knowledge on alcohol-related fact, alcohol expectancy and subjective drinking norm.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.285-292
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• 2014

A named entity recognition method is used to improve the performance of information retrieval systems, question answering systems, machine translation systems and so on. The targets of the named entity recognition are usually PLOs (persons, locations and organizations). They are usually proper nouns or unregistered words, and traditional named entity recognizers use these characteristics to find out named entity candidates. The titles of books, movies and TV programs have different characteristics than PLO entities. They are sometimes multiple phrases, one sentence, or special characters. This makes it difficult to find the named entity candidates. In this paper we propose a method to quickly extract title named entities from news articles and automatically build a named entity dictionary for the titles. For the candidates identification, the word phrases enclosed with special symbols in a sentence are firstly extracted, and then verified by the SVM with using feature words and their distances. For the classification of the extracted title candidates, SVM is used with the mutual information of word contexts.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.55-63
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• 2017

One of the purposes of Information Technology (IT) is to support human response to natural and social problems such as natural disasters and spread of disease, and to improve the quality of human life. Recent climate change has happened worldwide, natural disasters threaten the quality of life, and human safety is no longer guaranteed. IT must be able to support tasks related to disaster response, and more importantly, it should be used to predict and minimize future damage. In South Korea, the data related to the damage is checked out by each local government and then federal government aggregates it. This data is included in disaster reports that the federal government discloses by disaster case, but it is difficult to obtain raw data of the damage even for research purposes. In order to obtain data, information extraction may be applied to disaster reports. In the field of information extraction, most of the extraction targets are web documents, commercial reports, SNS text, and so on. There is little research on information extraction for government disaster reports. They are mostly text, but the structure of each sentence is very different from that of news articles and commercial reports. The features of the government disaster report should be carefully considered. In this paper, information extraction method for South Korea government reports in the word format is presented. This method is based on patterns and dictionaries and provides some additional ideas for tokenizing the damage representation of the text. The experiment result is F1 score of 80.2 on the test set. This is close to cutting-edge information extraction performance before applying the recent deep learning algorithms.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.187-197
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• 2020

Recently, various methods of text embedding using deep learning algorithms have been proposed. Especially, the way of using pre-trained language model which uses tremendous amount of text data in training is mainly applied for embedding new text data. However, traditional pre-trained language model has some limitations that it is hard to understand unique context of new text data when the text has too many tokens. In this paper, we propose self-supervised learning-based fine tuning method for pre-trained language model to infer vectors of long-text. Also, we applied our method to news articles and classified them into categories and compared classification accuracy with traditional models. As a result, it was confirmed that the vector generated by the proposed model more accurately expresses the inherent characteristics of the document than the vectors generated by the traditional models.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.585-592
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• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.453-459
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• 2022

A decrease in fertility rates causes problems such as decrease in the working-age population, and has a significant impact on national policies. Currently, the Republic of Korea has a conscription system that imposes military service on all men over the age of 18. However, the transition to the volunteer miliatry system is emerging as a social issue due to the decrease in the fertility rate. In this paper, news articles and comments searched for through the keyword ' volunteer miliatry system' were collected to analyze the social perception of the volunteer miliatry system from 2018, when the fertility rate dropped to less than 1. Some of the collected comments were labeled, and emotional levels were calculated through deep learning models. Through this study, we found that awareness of recruitment system conversion did not increase as the decrease in the fertility rate, and it was confirmed that people's interest is gradually increasing.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.