• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Hospice and Palliative Care
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• v.8 no.2
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• pp.143-151
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• 2005

Purpose: Bereavement Memorial Service has been held every year by the hospice team at St. Vincent's Hospital for the purpose of supporting the bereaved family who feel grief and mourning. The purpose of this study is to find out the attitude of the bereaved attending at bereavement memorial service (BMS) and to find out the areas needing improvements to set up better memorial service. Methods: Hospice team sent invitation card to 180 families of patients who admitted and passed away at hospice ward Nov., 2003${\sim}$Oct., 2004. Among them 22 families attended the BMS meeting, which was held on 26th Nov., 2004. The researcher collected data from 22 families with 'Questionnaire' survey. Except identifying data and 2 dichotomy questions, we used open-ended questionnaire. 1 researcher conducted a telephone interview survey in 18 families who couldn't attend at BMS meeting. Results: The median age was 56 (range $16{\sim}19$) and there were 37 females and 3 males. They were patient's wife (22), mother (4), husband (5), daughter (4), mother-in-law (1), siblings (1), brothers wife (1). Duration after bereavement, $1{\sim}3$ months (17) was the highest frequency. 36 families agreed 'the dead experienced the death with dignity'. The reason of agreement to the death with dignity was 'the patient died in preparation' (16). 'the patient died in well-being condition spiritually' (9), 'the patient died in comfort physically (7). 4. persons thought the dead died with indignity. The bereaved defined 'the death with dignity' as follows: 'acceptance of death & death in spiritual well-being' (9), 'death in physical comfort condition' (7), 'the death in psycho-social well-being' (3), non-respondents (10). Most families (21) were still in difficulty to overcome bereavement grief. The answer regarding the method to overcome the difficulty was 'with spiritual sublimation' (13), 'with devotion of oneself in daily life' (10), 'with devotion to mourning as it is' (3). With regard to their attitude to invitation, 'having joy and thanks from hospice team' (21), 'grief' (4), 'suffering' (4). Toward the existence of hesitation about attendance at BMS meeting, the result as follows. Nonexistence of hesitation respondent (34), existence respondent (6), the reason for hesitation was various; 'the meeting reminds me of the suffering times', 'the meeting makes me to recall, and it will be likely to cry', and so on. The needs and feelings to memorial service meeting were various; 'it was meaningful time', 'it was good to recall about the dead', 'more meeting annually' and so on. In respect of the most difficulty after bereavement, in attendant family, 'depression' (10) was the highest frequency, whereas, in non-attendant family, the most difficult thing was 'financial problem/role difficulty (6). Conclusion: This study shows the rate of attendance was high in bereaved whose bereavement duration $1{\sim}3$ month. Most of bereaved were still suffering from bereavement grief within 1 year. Although most families didn't hesitate and felt positive mood to invitation, the rate of attendance was low. Comparing with two groups between attendant family and non-attendant, the latter felt more difficulty in 'financial problem/role difficulty, on the other hand, the former felt difficulty in 'depression'. Hereafter, the additional study about the factor relating to these attitude and needs of the bereaved relating to memorial service will be necessary.

• Journal of the Korean Society of School Health
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• v.22 no.1
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• pp.1-16
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• 2009

Purposes: The objective of this study was to develop a management program for Korean elementary school students suffering from asthma, which would be based on the Australian Asthma-Friendly Schools (AFS) program. Methods: On the basis of the AFS program, we designed a 6-month pilot project for asthmatic students in two elementary schools in a rural area and one elementary school in an urban area of Korea. The pilot project consisted of the following processes: identifying students with asthma in a school, educating school staffs and the parents of an asthmatic child, registering those with asthma, and installing emergency kits for asthma attacks in school health rooms. In order to evaluate these processes, group discussions were held between project team members and school staffs in each area. In addition, we conducted a postal survey of 144 households having an asthmatic child. Results: The screening process adopted in this program resulted in the early diagnosis in asthma; however, it needs to be evaluated economically due to expensive diagnostic test for asthma. For the school nurses, asthma lessons were evaluated as being very helpful for their tasks, while teachers tended to take less interest in the program with only 45% of all teachers attending these lessons. Almost all participating parents reported that such lessons would be beneficial for the care of their child, even though only 24.2% of the survey respondents (122 households) attended the lessons. Installing emergency kits in school health rooms was regarded as a key feature of this project. The introduction of a register card containing more specific health records of asthmatic students was considered necessary to replace the existing list of students with asthma. Conclusion: This study has merit in that a Korean asthma-friendly schools program was developed for the first time, despite the many obstacles to such programs becoming more common.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.