• Journal of Korean Academy of Nursing
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• v.23 no.3
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• pp.467-486
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• 1993

Today, more chronically ill and handicapped people are being cared for at home by a family member caregiver. The task of caring for a family momber may mean that the caregiver has less time and money and more work which may result in increased fatigue and symptoms of illness. This study was done to examine the well-being of family caregivers. Fifty three family caregivers were interviewed. Concepts were measured using existing tools and included : Burden(25 item 5 point scale), Social sup-port (21 item 7 point scale), Health status defined by a symptom checklist(48 item S point scale), and Well -being defined by a quality of life scale (14 item 7 point scale) and caregiving activities. Data collection was done by interview and Q-sort. Social support and well - being were positively correlated as were symptoms and burden. Symptoms and burden were negatively correlated with social support and well-being. Items on the quality of life scale had a mean score range from 3.09 to 4.96. Quality of life related to income was lowest (3.09) but the desire to use more money for the patient was rated 2.90 on the burden scale where the item means ranged from 0.73 to 3.55. The high mean of 3.55 was for obligation to give care and the low 0.73 was (or not feeling that this was helping the patient. Mean scores for symptoms ranged from 0.26 to 2.15 with the 2.15 being for “worry about all the things that have to be done.” Over half of the patients were dependent for help with some activities of daily living. The caregivers reported doing an average of 3.40 out of five patient care activities including bathing (77.4%), shampooing (67.9%), and washing face and hands (49.1％), and 3.74 out of seven home maintenance activities including laundry (98.1％), cooking (83.0%), and arranging bed-ding(75.5%). The caregivers reported their spouse as one of the main sources of social support, including in times of loneliness and anger The mean score for loneliness as burden was 2.15 and ranked fourth and 31 (58.5％) of the sample reported being lonely recently and not being satisfied with the support received. Similarly anger caused by the patient was given a mean score of 2.13, and anger was reported to have been present recently by 38 (71.7%) of the sample and satis-faction with the support given was low. Having someone to help deal with anger ranked twelfth out of 21 items on the social support scale and had a mean score of 3.98 (range 3.49 to 5.98). Spouses were reported as a major source of social support but the fact that 50% of the caregivers were caring for a spouse, may account for the quality of this source of social support having been affected. These caregivers faced the same problems as others at the same stage of life. but because of the situation, there was a strain on their resources, particularly financial and social. In conclusion it was found that burden is correlated negatively to quality of life and positively to symptoms, but in this sample, symptoms and bur-den were scored relatively low. Does this indicate that the caregivers accept caregiving as part of their destiny and accept the quality of their lives with burden and symptoms just being a part of caregiving\ulcorner Does the correlation between the bur-den and symptoms indicate they are a measure of the same phenomenon or that the sample was of a more mobile, less burdened group of caregivers\ulcorner Quality of life was the one variable that was significant in explaining the varience on burden. Further study is needed to validate the conclusions found in this study but they indicate a need for nurses to ap-proach these caregivers with a plan tailored to each individual situation and to give consideration to interventions directed at improving quality of life and expanding social support networks for those caring for spouses.

• Journal of muscle and joint health
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• v.3 no.1
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• pp.37-49
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• 1996

The joint diseases threaten modern people's healthy life. They bring about a long pain, an anasarca, loss of joint function or even deformation and rigidity of joint, limiting people's ordinary activities much. The chronic joint patients may be subject to some hypochondria caused by anxiety for their life, social isolation, financial problem and physical disability. Therefore, this population should continue to be duely taken care of by medical personnels. In particular, nurses should adequately help these people to recover and improve their health through suitable adaptations. With such basic conceptions in mind, this study was aimed at reviewing these patients' conditions in their ordinary life before and after a plastic operation for artificial joint replacement as well as their satisfaction with their life. For this purpose, those patients who underwent some plastic operations for artificial joint replacement at university hospitals in Seoul from January 2, 1993 to June 30, 1995 were selected as the population of this study. Among them, 87 people were randomly sampled to answer a questionnaire designed specially. For the surveying tools, Jette's (1980) scale was applied to address the sample people's inconveniences experienced and supports received in their ordinary life, while the scale of Wood, Wylie & Sheafer was used to measure their satisfaction with their life. The collected data were analyzed for percentiles, means, SD, t-test and Pearson's correlations. The results of survey can be summarized as follows ; As a result of t-test the frequencies of other people's support before and after the plastic operation, it was disclosed that those who underwent the operation were supported less frequently. In addition, as a result of t-testing their satisfaction with life before and after the operation, it was found that the operation increased their satisfaction with life significantly. Meanwhile, as a result of t-test inconveniences, frequencies of supports and life satisfaction before and after the plastic operation for artificial knee replacement, it was disclosed that only the inconveniences were significantly reduced after the operation. In contrast, the t-test the variables before and after the plastic operation for artificial hip replacement, it was found that only the frequencies of other people's supports were significant reduced after the operation. Furthermore, the differences 6 months, one year and two years after the plastic operation for artificial joint replacement were t-tested on the variables. As a result, it was disclosed that people's inconvenience, frequencies of supports and life satisfaction were not improved 6 months after the operation but their frequencies of supports decreased significantly one year after, while their inconveniences and life satisfaction were significantly improved two years after. As a result of analyzing the variables with Pearson's correlations, inconveniences and frequency of supports were negatively correlated significantly with the life satisfaction. In conclusion, the plastic operation for artificial joint replacement significantly improved people's living inconveniences, reduced their frequency of other people's support and enhanced their satisfaction with life. To break don the plastic operation for artificial knee replacement improved patients' inconveniences, while the plastic operation for artificial hip replacement not only improved patients' inconveniences but reduced the frequencies of other people's support also. Finally, the finding that the plastic operation for artificial joint replacement brought about the improvement two years after suggests that this period is needed for the patients to adapt themselves to the post-operation conditions.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Korean Journal of Culture and Social Issue
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• v.22 no.4
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• pp.487-522
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• 2016

The main purpose of this research is to investigate Korean adolescents' perception of stress experiences, and related social support. To this end, adolescents were asked about stress events, as well as stress symptoms, in their lives. Also, the adolescents were asked about the people that provided social support and the types of social support provided. The participants were 952 Korean adolescents (Primary 219; Middle 280; High 212; University 241). Among the four measures (stress events, stress symptoms, social supporters, and types of social support), the measure of stress symptoms yielded a reliability of Cronbach α=.88, while the remaining three measures yielded an inter-judger reliability of 89.6%, Kappa=.87. The results were as follows. First, for stress events, the most frequent responses were related to Academic Achievement, followed by Career/Job, Family Relations, Friend Relations, Lack of Capacity, and Financial Difficulties. For high-school students the most frequent responses were related to Academic Achievement, while for university students Career/Job. Second, for stress symptoms there were significant differences among the groups, in that the high-school students showed the highest level of symptoms, while primary school students the lowest. Third, for social supporters, the most frequent responses were related to Friends, followed by Myself, Parents, Teacher, Siblings, and Seniors/Juniors. As the groups aged (from primary to university), support from Friends and Seniors/ Juniors increased, while support from Parents decreased. Fourth, for the types of social support, the most frequent responses were related to Emotional Support, followed by None, Advice, Supporter Directly Solved Problem, and Talked with Me. The highest frequencies of responses were found for Emotional Support among all groups. As the groups aged (from primary to university), Advice increased while Supporter Directly Solved Problem decreased.

• The Korean Society of Law and Medicine
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• v.24 no.2
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• pp.35-77
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• 2023

As the amendment to the Personal Information Protection Act, which newly established the basis for the right to request transmission of personal information, was promulgated through the plenary session of the National Assembly, MyData, which was previously applied only to the financial sector, could spread to all fields. The right to request transmission of personal information is the right of the information subject to be guaranteed for the realization of MyData. However, since the right to request transmission of personal information stipulated in the Personal Information Protection Act is designed to be applied to all fields, not a special field such as the medical field, it has many shortcomings to act as a core basis for implementing MyData in Medicine. Based on this awareness of the problem, this paper compares and analyzes major legal trends related to the right to portability of personal health information at home and abroad, and examines the limitations of Korea's Personal Information Protection Act and Medical Act in realizing Medical MyData. Under the Personal Information Protection Act, the right to request transmission of personal information is insufficient to apply to the medical field, such as the scope of information to be transmitted, the transmission method, and the scope of the person obligated to perform the transmission, etc.. Regulations on the right to access medical information and transmission of medical records under the Medical Act also have limitations in implementing the full function of Medical My Data in that the target information and the leading institution are very limited. In order to overcome these limitations, this paper prepared a separate and independent special law to regulate matters related to the use and protection of personal health information as a measure to improve the legal system that can effectively guarantee the right to portability of personal health information, taking into account the specificity of the medical field. It was proposed to specifically regulate the contents of the movement and transmission system of personal health information.

• Korean Journal of Culture and Social Issue
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• v.12 no.5_spc
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• pp.45-79
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• 2006

The purpose of this paper was to review the results of the subjective well-being(swb) studies performed by Hahn and coworkers in Korean culture. As the correlates of swb, we dealt with demographic/individual difference variables, intrapersonal variables, interpersonal process variables, and Korean cultural variables. We proposed that the components of swb were consisted of quality of life(cognitive swb) and overall happy feelings about one's own life(emotional swb). It was also assumed that a measure of total swb could be calculated by summated mean of cognitive swb and emotional swb measures. The data of the swb studies were analyzed and interpreted according to the above three measures of swb. The results of a nationwide survey(Hahn, 2004) from age of 19 to 75 years ald(n=2,230) showed significant simple correlation coefficients between the following demographic/individual difference variables and swb: Gender difference in swb was found(total swb r=.08, p<.001; life satisfaction r=.10, p<.001; overall emotional swb r=.05, p<.05). Men were happier than women in terms of all three measures of swb. It was also found that women appeared to experience greater positive and negative emotions. Correlation between age and emotional swb(r=.09, p<.001) was significant, but life satisfaction was not significant(r=.04, n.s). Correlations between economic status and swb were also significant(total swb r =.23, p<.001; life satisfaction r=.15 p<.001; overall emotional swb r=.15, p<.001l). Although existence of father was negatively related to emotional swb(r=-.05, p<.05), the existence of mother was not related to any of swb measures. Similarly existence of brothers was related positively to overall emotional swb, but existence of sisters was not. Though existence of son was not related to swb, daughter contributed negatively to swb(total swb -.12, p<.01; life satisfaction -.09, p<.05; emotional swb r=-.12, p<.01). We assumed that family member-in-Iaw also contributed to swb because the extended dose social networks were important in Korean culture. The results showed that the following family member-in-law variables were related to swb: Parents-in-law(total swb r=.11, p<.01; life satisfaction r=.10, p<.01; emotional swb r=.10, p<.01), father-in-law(total swb r=.11, p<.01; life satisfaction r=.11, p<.01; emotional swb r=.06, n.s). The result suggested that especially father-in-law contributed to swb through financial and social support. Correlations between emotional experiences in everyday life and swb were also presented. The range of correlation coefficients between the positive emotion measures and swb were r=.30~.48(p<.001) when the above two measures obtained at same time. But the range decreased to r=.19~32(p<.001) when the swb measure was obtained 9 month later longitudinally. Intercorrelations between positive emotional experience; and life satisfaction were r=.37~58(p<.001) when two measures were obtained at same time. We also examined the effects of the intrapersonal cognitive responses to the most stressful life event upon swb. The results of nationwide survey(n=1,021) showed that self-disclosure(total swb r=.09, p<.010; life satisfaction r=.10, p<.01; emotional swb r=.07, p<.01), rumination(total swb r=-.17, p<.001), thought avoidance(total swb r=.12, p<.001; life satisfaction r=-.08; emotional swb r=-.12, p<.001) and suppression(total swb r=-.13, p<.001; life satisfaction r=-.08, p<.05: emotional swb r=-.13, p<.001) contributed to swb. It was also suggested that mismatch between self-guide and regulatory focus contributed negatively to emotional swb. It was also found that social comparison motives and fulfillment of the motives contributed to swb. The results of a survey research(n=363 college students) revealed that the higher the general social comparison motive, the lower the swb(total swb r=-.15, P<.01: life satisfaction r=-.17. p<.01; emotional swb r=-.10, p<.05). It was also found that satisfaction level of self-evalution motive contributed positively to swb(total swb r=-.14. p<.01: life satisfaction r=-.12, p<.05; emotional swb r=.15, p<.001). Both of self-improvement motive(r=.13, p<.05) and satisfaction level of self-improvement motive(r=.12, p<.05) contributed positively to emotional swb, respectively. The above results suggested that swb was depended upon the interaction effect of social comparison motive; and level of fulfillment of the motives. We also reported the significant multiple predictors of swb in a sample of age from 60years to 89years olds. The results of multiple regression analysis showed that the significant multiple predictors of swb were past illness(β=.174, p<.001), economic status(β=.418, p<.001), marital satisfaction(β=.0841, p<.001), satisfaction of offsprins(β=.065, p<.01), expectation level of social support from offsprings(β=-.049, p<.001), and negative emotions(β=-.454. p<.001) among 16 social psychological factors. It was also found that swb was an important multiple predictors of physical health. This finding was replicated in a longitudinal study. Both of positive and negative emotional experiences were significant multiple predictors of physical health one year later. The results of the discriminant analysis showed both of total swb and positive emotional experiences contributed to discriminate the happy and healthy olds from unhappy and unhealthy olds. We paper also examined the effects of the nonnative social behaviors upon swb in Korean culture. The main hypotheses of the study(Hahn, 2006, in press) was that the important nonnative behaviors would influence on swb through both of the mediation processes of adjustment to social relationships and psychological stress. The survey data were collected from 2,129 adults age of 19 to 75, from 7 regional areas in Korea. The results of the study revealed that almost all of correlation coefficients between 15 normative social behaviors and the above three criteria w-ere significant. The fitness test results of the covariance structural equation model showed that all of the fitness indices were satisfactory (GFI=.974, AGFI=.909, NNFI=.922, NFI=.973, CFI=.974. RMR=.049, RMSEA=.073). The results of the analysis revealed that the following five path coeffi6ents from behaviors to social adjustment were significant; behavior tor family and family members(t=5.87, p<.001), courteous behavior(t=4.39, p<.001), faithful behavior (t=2.15. p<.05). collectivistic behavior(t=8.31, p<.001). Seven path coefficients from the normative behaviors to psychological stress were significant; behavior for family and family members (t=-4.63, p<.001), faithful behavior(t=-3.86, p<.001). suppression of emotional expression(t=3.99, p<.001), trustworthy and dependable behavior(t=-2.21, p<.05), collectivistic behavior(t=3.72, p<.001), effortful and diligent behavior(t=2.94, p<.001), husbandry and saving behavior(t=3.40, p<.001). The above results suggested that four normative behaviors among seven behaviors contributed negatively to psychological stress in current Korean society. The results abo confirmed the hypothesized paths from social adjustment (t=10.40, p<.001) to swb and from psychological stress(t=-19.74, p<.001) to swb. The important results of the study were discussed in terms of the Confucian traditions and recent social changes in Korean culture. Finally limitations of this review paper were discussed and the suggestions for the future study were also proposed.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.