• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.19-33
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• 2020

This study aims to analyze on the changes of opinion in terms of Sejong City construction using big data. The research data are newspaper articles related to the argument of construction in Sejong City. The newspaper article data was reported by Hankyoreh, Dong-A Ilbo and Hankook Ilbo. The arguments related to the construction of Sejong City was included the new administrative capita, multifunctional administrative city and amendments of Sejong City. The analysis method used in this study is frequency analysis, sentiment analysis and social network analysis using python and gephi 0.9.2 programs. The results of the analysis are as follows. First, as a result of frequency analysis, the keywords of Hankyoreh showed the characteristics of consent - consent - dissent according to the construction period of Sejong City. The Dong-A Ilbo showed positions of dissent - dissent - consent. In addition, the Hankook Ilbo was analyzed to have the characteristics of dissent - consent - dissent tendency. Secondly, results of sentiment analysis, The Hankyoreh showed positive - positive - negative tone. The characteristic of Dong-A Ilbo is that the focus has changed from negative to negative to positive. The Hankook Ilbo showed that changed from negative to positive to negative. Finally, the results of social network analysis are as follows. At the time of the construction of Sejong City, each opinion of media was showed a changes in issues according to political and ideological characteristics such as conservative, progressive and moderation. In detail, Hankyoreh focused on balanced regional development. The Dong-A Ilbo represented the opinion of the Conservative Party. The Hankook Ilbo was highlighting the issues confronting the conservative party and progressive party during the construction of Sejong City.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.149-157
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• 2010

As the use of the Internet has recently increased, the demand for opinion information posted on the Internet has grown. However, such resources only exist on the website. People who want to search for information on the Internet find it inconvenient to visit each website. This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposed technologies proved to have outstanding capabilities in comparison to existing ones through tests. The capabilities to extract positive and negative opinion information were assessed. Specifically, test movie review sentence testing data was tested and its results were analyzed.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.563-569
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• 2012

The studies of opinion mining or sentiment analysis have been the focus with social web proliferation. Sentiment analysis requires sentiment resources to decide its polarity. In the existing sentiment analysis, they have been built resources designed with intensity of sentiment polarity and decided polarity of opinion using the ones. In this paper, I will present sentiment categories for not only polarity of opinion but also the basis of positive/negative opinion. I will define psychological emotions to primary sentiments for the reasonable classification. And I will extract the informations of sentiment from social web texts for the actual distribution of sentiments in social web. Re-classifying primary sentiments based on extracted sentiment information, I will organize sentiment categories for the social web. In this paper, I will present 23 categories of sentiment by using proposed method.

• Information Systems Review
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• v.22 no.4
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• pp.59-74
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• 2020

This paper investigates the asymmetric effect of social sentimental on an individual stock price return. For this purpose, four companies such as POSCO, Korean Electricity, AMORE PACIFIC, KIA Motors are chosen from KOSPI listed companies in terms of dataperspective. The main estimation results are as follows: the positive opinions affect only the stock prices return of three companies while the negative opinions affect all of the companies. It shows that positive or negative texts give asymmetric effect on stock price return and the effect of negative opinions is bigger than that of positive opinions. The results imply that investors are more sensitive to the negatives since they have the tendency of loss aversion. Also, it indicates that subjective opinion on SNS can be used as the proxy for the investment sentiment.

• Asian Journal for Public Opinion Research
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• v.12 no.2
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• pp.75-101
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• 2024

This study investigates the variation in anxiety levels across income classes in the wake of the COVID-19 pandemic in Indonesia. The research is based on data from nationally representative surveys conducted in 2017 and 2021, and it employs a multilevel mixed-effects ordered logistic model. The unique aspect of this investigation lies in its utilization of the Cantril ladder, a commonly employed tool in public opinion research, to gauge anxiety levels. Participants are prompted to assess their present life circumstances concerning their daily worries and anxieties. The empirical findings provide evidence that individuals in provinces with higher exposures to COVID-19 reported heightened anxiety levels. Furthermore, the results highlight a consistent association between higher household income and lower levels of anxiety. Notably, individuals from the highest income group experienced a substantial decline in anxiety levels during the pandemic. When examining specific income classes, the study reveals heightened anxiety among women in higher-income brackets and among lower-income households residing in urban areas. Furthermore, regarding macroeconomic circumstances, the results illustrate a positive correlation between economic prosperity and anxiety levels among members of low-income households. The study also uncovers a positive connection between income inequality and self-assessed anxiety within upper-middle and high-income brackets.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.243-253
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• 2012

An opinion mining technique which was developed from document classification in area of data mining now becomes a common interest in domestic as well as international industries. The core of opinion mining is to decide precisely whether an opinion document is a positive or negative one. Although many related approaches have been previously proposed, a classification accuracy was not satisfiable enough to applying them in practical applications. A opinion documents written in Korean are not easy to determine a polarity automatically because they often include various and ungrammatical words in expressing subjective opinions. Proposed in this paper is a new approach of classification of opinion documents, which considers only a frequency of word patterns and excludes the grammatical factors as much as possible. In proposed method, we express a document into a bag of words and then apply a learning algorithm using a frequency of word patterns, and finally decide the polarity of the document using a score function. Additionally, we also present the experiment results for evaluating the accuracy of the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.6
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• pp.871-879
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• 2010

In this paper, we present a system that classifies comments on a news article into a user opinion called a polarity (positive or negative). The system is a kind of document classification system for comments and is based on machine learning techniques like support vector machine. Unlike normal documents, comments have their body that can influence classifying their opinions as polarities. In this paper, we propose a feature weighting scheme using such characteristics of comments and several resources for opinion classification. Through our experiments, the weighting scheme have turned out to be useful for opinion classification in comments on Korean news articles. Also Korean character n-grams (bigram or trigram) have been revealed to be helpful for opinion classification in comments including lots of Internet words or typos. In the future, we will apply this scheme to opinion analysis of comments of product reviews as well as news articles.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.225-232
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• 2021

Nowadays, opinion leaders influence the formation of public opinion on various issues in social network services. There has been a lack of research on the personal characteristics that inspire users to interact with opinion leaders and show intent to act. This paper verifies how the disposition of Facebook users' self-identity affects the quasi-social interaction with opinion leaders on Facebook and the intention to share information. As the perception and behavior of users on social media platforms differ depending on the type of issue, an online survey was conducted by classifying issue types into life culture and political sectors. Research found that personal identity had a significant positive effect on quasi-social interactions in the life culture and politics sectors, while group identity negatively affected quasi-social interactions. In addition, the intention to share information was confirmed to have a significant effect only in the life and culture areas of self-identity (social and group identity). Quasi-social interaction was confirmed to have a significant positive effect on all issue areas. The results of this study suggest the need to consider variations in opinion leader marketing strategies based on the types of self-identity of Facebook users in the future. In addition, the study shows that raising the level of quasi-social interaction at the corporate level without distinction of issue types can lead to effective results.

• Women's Health Nursing
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• v.12 no.3
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• pp.193-203
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• 2006

Purpose: The purpose of this study was to investigate current circumstances of violence against wives, and to identify the wife's opinion of the spouse's punishment in domestic violence cases. Method: The subjects were 216 married women in G province. Data was gathered from November 22 to December 6, 2004. Data was analyzed by descriptive statistics, and the $x^2$-test using SPSS/Win 10.0 program. Results: About thirty six percent of the subjects had experience with domestic violence. There was a high prevalence of psychological aggression(68%), sexual coercion (36%), physical assault(31%), and injury(19%). The subjects experiencing domestic violence had a higher positive attitude towards the spouse's punishment than subjects not experiencing domestic violence. The more severe the domestic violence was, the more the battered women's positive attitude for criminal action increased. Conclusion: An educational program and public relations will increase women's empowerment to solve domestic violence. A more cooperative and integrative program for prevention and an intervention system against domestic violence should be developed for women in battered situations.