• The Journal of Korean Medical History
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• v.24 no.1
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• pp.43-56
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• 2011

Everyone once in a life experience headaches as symptoms are very common. According to a study in a country of more than a week and as many as those who have experienced a headache amounts to 69.4%. In addition, the high reported prevalence of migraine in 30s for 80% of all migraine sufferers daily life interfere with work or was affected. In Western medicine, the cause of headaches is traction or deformation of pain induced tissue like scalp, subcutaneous tissue, muscle, fascia, extracranial arteriovenous, nerves, periosteum. But it turns out there are not cause why pain induced tissue is being tracted or deformated. Therefore, most of the western-therapy is mainly conducted with regimen for a temporary symptom reduction. Therefore, I examined how it has been developed in Chinese Ming Dynasty, the perception of headache, change in disease stage and an etiological cause. Oriental medicine in the treatment of headache is a more fundamental way to have an excellent treatment. The recognition of head in "素問($s{\grave{u}}$ $w{\grave{e}}n$)" and "靈樞($l{\acute{i}}ng$ $sh{\bar{u}}$)" began to appear in 'Soul-神($sh{\acute{e}}n$) dwelling place' and 'where to gather all the Yang-'諸陽之會($zh{\bar{u}}$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $hu{\grave{i}}$)'. Also, head was recognized as '六腑($li{\grave{u}}f{\check{u}}$) 淸陽之氣($q{\bar{i}}ng$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $q{\grave{i}}$) and 五臟($w{\check{u}}$ $z{\grave{a}}ng$) 精血($j{\bar{i}}ng$ $xu{\grave{e}}$) gathering place'. More specific structures such as the brain is considered a sea of marrow(髓海-$su{\check{i}}$ $h{\check{a}}i$) in "內經($n{\grave{e}}i$ $j{\bar{i}}ng$)" and came to recognized place where a stroke occurs. Accompanying development of the recognition about head, there had been changed about the perception of headache and the recognition of the cause and mechanism of headache. And the recognition of headache began to be completed in Ming Dynasty through Jin, Yuan Dynasty. Chinese Ming Dynasty, specially 樓英($l{\acute{o}}u$ $y{\bar{i}}ng$), in "醫學綱目($y{\bar{i}}xu{\acute{e}}$ $g{\bar{a}}ngm{\grave{u}}$)", first enumerated prescription in detail by separating postpartum headache. and proposed treatment of headache especially due to postpartum sepsis(敗血-$b{\grave{a}}i$ $xu{\grave{e}}$). 許浚($x{\check{u}}$ $j{\grave{u}}n$) accepted a variety of views without impartial opinion in explaining one kind of headache in "東醫寶鑑($d{\bar{o}}ng-y{\bar{i}}$ $b{\check{a}}oji{\grave{a}}n)$" 張景岳($zh{\bar{a}}ng$ $j{\check{i}}ng$ $yu{\grave{e}}$), in "景岳全書($j{\check{i}}ng$ $yu{\grave{e}}$ $qu{\acute{a}}nsh{\bar{u}}$)", established his own unique classification system-新舊表裏($x{\bar{i}}nji{\grave{u}}$ $bi{\check{a}}ol{\check{i}}$)-, and offered a clear way even in treatment. Acupuncture treatment of headache in the choice of meridian has been developed as a single acupuncture point. Using the classification of headache to come for future generation as a way of locating acupoints were developed. Chinese Ming Dynasty, there are special treatments like 導引按蹻法($d{\check{a}}o$ y ${\check{i}}n$ ${\grave{a}}n$ $ji{\check{a}}o$ $f{\check{a}}$), 搐鼻法($ch{\grave{u}}$ $b{\acute{i}}$ $f{\check{a}})$, 吐法($t{\check{u}}$ $f{\check{a}}$), 外貼法($w{\grave{a}}i$ $ti{\bar{e}}$ $f{\check{a}}$), 熨法($y{\grave{u}}n$ $f{\check{a}}$), 點眼法($di{\check{a}}n$ $y{\check{a}}n$ $f{\check{a}}$), 熏蒸法($x{\bar{u}}nzh{\bar{e}}ng$ $f{\check{a}}$), 香氣療法($xi{\bar{a}}ngq{\grave{i}}$ $li{\acute{a}}of{\check{a}}$). Most of this therapy in the treatment of headache, it is not used here, but if you use a good fit for today's environment can make a difference.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.1
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• pp.23-33
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• 2012

In this study, how much users of Hannamkeumbuk Keumbuk Mountains are aware of Baekdaegan and its attached mountain chains, a traditional geographic system, according to Sangyungpyo and basic data like the degree of awareness and use-behaviors, etc. have been studied. In addition, the environmental value of Hannamkeumbuk Keumbuk Mountains separating the central and the southern part of Korea among attached mountain ranges, secondary mountain chains, which act as an ecosystem buffer in the Baekdudaegan Range, has been estimated at the current amount of currency. In the questions of the perception of the traditional classification standard of mountain chains and Baekdudaegan, more than 70% of respondents answered that they had heard of or known them but 66.8% werenot aware of Hannamkeumbuk Keumbuk Mountains. While the awareness for Baekdudaegan is high, the perception of its attached mountain chains was very poor. DBDC responder system and CVM, which is used widely for the value estimate method of environment goods, were used. As the result, an additional benefit got when a person visits Hannamkeumbuk Keumbuk mountains was estimated as 5,813 won. It could find out that this amount was very low compared with 51,984 won, average visit cost. It judged that the reason was that damage of environmental conditions, the monotony of the trails and progress of indiscriminate environmental destruction, etc. The results of this study will offer a new perspective on public relations activities and resource conservation of Baekdudaegan and its attached mountain chains and estimate perceptions and efficient services for visitors to HannamKeumbuk Keumbuk Mountains. This study will act as data for basic planning and management to increase the mountains' value and to preserve them. Further studies are needed to make a frame of work division and management with various organizations so that the management of Hannamkeumbuk-Keumbuk Mountains may be properly established and their value may been hanced.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.51-60
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• 2014

In this study, ICT medical service provider's level of knowledge fire fighting safety and methods on coping with fires in the regions of Gwangju and Jeonam Province of Korea were investigated to determine the elements affecting such levels and provide basic information on the manuals for educating how to cope with the fire fighting safety in medical facilities. The data were analyzed using SPSS Win 14.0. The scores of level of knowledge fire fighting safety of ICT medical service provider's were 7.06(10 point scale), and the scores of level of recognizing how to cope with fire fighting safety were 6.61(11 point scale). level of recognizing how to cope with fire fighting safety were significantly different according to gender(t=4.12, p<.001), age(${\chi}^2$=17.24, p<.001), length of career(${\chi}^2$=22.76, p<.001), experience with fire fighting safety education(t=6.10, p<.001), level of subjective knowledge on fire fighting safety(${\chi}^2$=53.83, p<.001). In order to enhance the level of understanding of fire fighting safety and methods of coping by the ICT medical service providers it is found that: self-directed learning through avoiding the education just conveying knowledge by lecture tailored learning for individuals fire fighting education focused on experiencing actual work by developing various contents emphasizing cooperative learning deploying patients by classification systems using simulations and a study on the implementation of digital anti-fire monitoring system with multipoint communication protocol, a design and development of the smoke detection system using infra-red laser for fire detection in the wide space, video based fire detection algorithm using gaussian mixture mode developing an education manual for coping with fire fighting safety through multi learning approach at the medical facilities are required.

• Journal of Korean Society of Forest Science
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• v.85 no.3
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• pp.396-408
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• 1996

The purposes of this study were to standardise the forest working system to design the intensity of working system in felling operation of the thinning forest in our country as well as to contrive the improvement of working method and the increase of productivity. For the purpose of investigating these, element working was classified by felling operation in softwood thinning forest, and a pulse rate were measured and analyzed. The results were as follow : 1. From the analysis of the pulse frequence measurment, the average pulse showed 108 pulse per minute for worker A in the total of pure working time, 130 pulse per minutes for worker B, 119 pulse per minute for worker C and 125 pulse per minute for worker D, respectively. 2. From the results of the pulse frequence analysis according to element working classification, the highest pulse frequence represented 115 pulse per minute for worker A in the circumference, 131 pulse per minute for worker B in the movement, 122 pulse per minute for worker C in the limbing operation and 128 pulse per minute for work D in hang-up. 3. If the original pulse frequence was 100% for workers, the working intensity showed as follow : worker A was 160%(original pulse frequence was 61=100%) for the total of the working intensity and 188% for the circumference among element working. Worker B was 220%(original pulse frequence was 57=100%) for the total of the working intensity and 229 for movement among element working. Worker C was 159%(original pulse frequence was 73=100%) for the total of the working intensity and 168% for limbing operation among the element working. Worker D was 156%(original pulse frequence was 70=100%) for the total of working intensity and 182% for hang-up among element working. 4. At the limit point of Labor performance rating, showing the total of working intensity, overtime pulse rate per minute was 30 for worker A, 207 for worker B, 14 for worker C and 67 for worker D. Worker B was highest in working intensity, and got physically a big load.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Research in Community and Public Health Nursing
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• v.12 no.1
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• pp.288-298
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• 2001

This article makes an attempt to evaluate the extent of developing community health nursing knowledge and to suggest the direction of developing a body of knowledge henceforth through the results of analysis for contents and outcomes of all literatures. which have been published in the Journal related to community health nursing. Refer to the following for the result of this article. 1. The total number of literatures analyzed amounted to 100 pieces in Journal of community health nursing society. 78 in Journal of industrial nursing society, 134 in Journal of school health society. 40 in Journal of home care nursing society. 2. Journal of community health nursing society Health needs and educational-behavioral diagnoses, which are more concrete nursing assessments and diagnoses. formed the main current(54%) of articles published in Journal of community health nursing society since 1992. There was a quantitative growth as well as a qualitative advance. Through a classification by the type of a body of knowledge. It was found that the knowledge providing nursing practice with bases, commanded an overwhelming majority(71.8%). Also, Researches on systemic supports for nursing practice are showing a tendency to increase. 3. Journal of industrial nursing society 52.6% of research papers presented in Journal of industrial nursing society dealt with health problem of workers. assessment of risk factors, diagnosis of health behaviors. Because of the beginning of an industrial nursing, the domain of nursing management to establish the role and task, work condition, training. documentary system made up 23 percent of research, subjects. A knowledge providing nursing practice with bases have a majority, 69.2%. In addition. the subject concerning a systemic support and quality assurance was scarce but continuously presented. 4. Journal of school health society The major point of this journal is the identification of health problems and risk factors which belong to assessment and diagnosis domain(56.8%) regardless of year, Because of the interdisciplinary characteristic. The knowledge on quality assurance of nursing practice is relatively rare. But, articles related to a systemic support is plentiful. 5. Journal of home care nursing society In its infancy, there was a large number of papers concerning need assessment and diagnosis, Comparing others, this journal has introduced a good many of articles related to program management. delivery system. service fee, etc that belong to domain of systemic support for nursing practice. 6. It is showing definitely that quantity and extent of research have grown for a short period. See the analysis in terms of nursing process, studies related to the domain of assessment and diagnosis command an absolute majority regardless of kinds of journal. Although articles referring to program management and implementation is increasing in number, it is scarce to evaluate a nursing program and grope for an improvement. Also, program development based on a theoretical framework is little. Therefore much more scientific effort to ensure profession should be executed. 7. In the methodological aspect, longitudinal study needs to be carried out so that we could show the evidence based nursing theory. To develop a more general theory, we have to conduct a study of various subjects and improve a validity of tools through a repeat test. In addition, the effort for interdisciplinary cooperation is needed.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.2
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• pp.23-39
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• 2017

This study began from the question, "is there a way to efficiently apply industrial demand in the university curriculum?" Research focused on how to actively accept and respond to the era of the NCS (National Competency Standards). In order to apply NCS to individual departments of the university, industrial personnel must positively participate to form a practical-level curriculum by the NCS, which can be linked to the work and qualifications. A valid procedure for developing a curriculum based on the NCS of this study is as follows: First, the university must select a specific classification of NCS considering the relevant industry outlook, the speciality of professors in the university, the relationship with regional industries and the prospects for future employment, and the need for industrial manpower. Second, departments must establish a type of human resource that compromises goals for the university education and the missions of the chosen NCS. In this process, a unique competency unit of the university that can support the basic or applied subjects should be added to the task model. Third, the task model based on the NCS should be completed through the verification of each competency unit considering the acceptance or rejection in the curriculum. Fourth, subjects in response to each competency units within the task model should be developed while considering time and credits according to university regulations. After this, a clear subject description of how to operate and evaluate the contents of the curriculum should be created. Fifth, a roadmap for determining the period of operating subjects for each semester or year should be built. This roadmap will become a basis for the competency achievement frame to decide upon the adoption of a Process Evaluation Qualification System. In order for the NCS to be successfully established within the university, a consensus on the necessity of the NCS should be preceded by professors, students and staff members. Unlike a traditional curriculum by professors, the student-oriented NCS curriculum is needed sufficient understanding and empathy for the many sacrifices and commitment of the members of the university.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.