• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Fashion Business
• /
• v.11 no.4
• /
• pp.42-51
• /
• 2007

The purpose of this study is to develop clothing design for mild dementia patients who display positive action in clothing for the improvement of the quality of life of the dementia patients following the symptoms of patients in accordance with the clinical classification to provide the functional assistance for ordinary living as well as emotional stability and aesthetic functions for the dementia elderly. The method of research is performed for theories through the advanced research and documentary data, and interpreted in functional and aesthetic level on the basis of the result of advance survey related to the characteristics of the mild dementia patients and clothing conduct of elderly with light dementia to select the material, color, decoration and functional design with four pairs for women and two pairs for men. Designs for the total of six have been actually produced by making the map, including the material swatch, color and others. The questionnaire as the measuring tool is used and the assessment category is made for the adaptability of design on each category. On the six clothes that are produced for the mild dementia patients, the statistics package SPSS Ver 12.0 is used for the data analysis on questions 8-10 for the frequency analysis. In overall, the leisure clothing for mild dementia patients developed from this research are generally satisfied, and overall type, material, color, detail and arrangement are generally evaluated highly, and have the assessment of normal or better in the color size and type.

• Journal of Korean Academy of Nursing
• /
• v.39 no.5
• /
• pp.683-692
• /
• 2009

Purpose: The purpose of this study was to identify cancer-related symptom clusters and to validate the conceptual meanings of the revealed symptom clusters in patients with hepatocellular carcinoma. Methods: This study was a cross-sectional survey and methodological study. Patients with hepatocellular carcinoma (N=194) were recruited from a medical center in Seoul. The 20-item Symptom Checklist was used to assess patients' symptom severity. Selected symptoms were factored using principal-axis factoring with varimax rotation. To validate the revealed symptom clusters, the statistical differences were analyzed by status of patients' performance status, Child-Pugh classification, and mood state among symptom clusters. Results: Fatigue was the most prevalent symptom (97.4%), followed by lack of energy and stomach discomfort. Patients' symptom severity ratings fit a four-factor solution that explained 61.04% of the variance. These four factors were named pain-appetite cluster, fatigue cluster, itching-constipation cluster, and gastrointestinal cluster. The revealed symptom clusters were significantly different for patient performance status (ECOG-PSR), Child-Pugh class, anxiety, and depression. Conclusion: Knowing these symptom clusters may help nurses to understand reasonable mechanisms for the aggregation of symptoms. Efficient symptom management of disease-related and treatment-related symptoms is critical in promoting physical and emotional status in patients with hepatocellular carcinoma.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.22 no.4
• /
• pp.318-327
• /
• 2012

The operating sound radiated from a laser printer includes tonal noise components caused by the rotating mechanical parts such as gear, shaft, motor, fan, etc. The negative effects of the tonal noise components need to be considered in the process of developing a sound quality index for the quantitative evaluation of the emotional satisfaction in terms of psycho-acoustics. However, in a previous paper, it was confirmed that the Aures tonality did not have enough correlation with the results of jury evaluation. The sound quality index based on loudness, articulation index, fluctuation strength has a little problem in considering the effects of rotating mechanical parts on the sound quality. In this paper, to solve the tonality evaluation problem, the calculation algorithm of Aures tonality was investigated in detail to find the cause of decreasing the correlation. The new tonality evaluation model was proposed by modifying and optimizing the masking effect, loudness ratio, and shape of weighting curve based on the basic algorithm of Aures tonality, and applied to two kinds of operating sound groups in order to verify the usefulness of proposed model. As a result, it is confirmed that the proposed tonality evaluation model has enough correlation and usefulness for expressing the tonalness in the operating sounds of laser printers. In the following paper, this results will be used to model the sound quality index as the input data by using the classification algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.4
• /
• pp.780-786
• /
• 2015

Social media, such as Social Network Service include a lot of spontaneous opinions from customers, so recent companies collect and analyze information about customer feedback by using the system that analyzes Big Data on social media in order to efficiently operate businesses. However, it is difficult to analyze data collected from online sites accurately with existing morpheme analyzer because those data have spacing errors and spelling errors. In addition, many online sentences are short and do not include enough meanings which will be selected, so established meaning selection methods, such as mutual information, chi-square statistic are not able to practice Emotional Classification. In order to solve such problems, this paper suggests a module that can revise the meanings by using initial consonants/vowels and phase pattern dictionary and meaning selection method that uses priority of word class in a sentence. On the basis of word class extracted by morpheme analyzer, these new mechanisms would separate and analyze predicate and substantive, establish properties Database which is subordinate to relevant word class, and extract positive/negative emotions by using accumulated properties Database.

• The Journal of the Korea Contents Association
• /
• v.18 no.8
• /
• pp.148-156
• /
• 2018

This study focuses on the play aspect of the existing digital games and VR games as part of a basic research for facilitating discussion on VR games. As a method for this research, the play aspect of digital games was classified based on the play theory by Johan Huizinga and Roger Caillois, followed by examination on the play characteristics of VR games according to the established classification. As a result, VR games share a significant portion of play characteristics with digital games. Nevertheless, in VR games the player is allowed unimpeded physical activity while having the HMD on and complete disconnection from the real world via new input/output devices. In the end, the transformation of play environment results in various sensory stimulation, and in turn influences the player's experiences in areas such as presence, immersion, emotional enjoyment and satisfaction. This study is expected to serve as an important indicator for studies on the characteristics of VR contents which is certain to develop in a wide range of directions in the future.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.229-234
• /
• 2009

The field of design is expanding and diversified in modern society while several new functions are presented by converging a variety of fields of knowledge. There are changes happening in people's way of thinking, sensitivity and behaviors as they are seeking for a new way of expression and aesthetic values. Accordingly, the black and white drawings are increasing as a new way of recognition of tradition or new materials. This paper is to make a study of the expression methods and their analysis of the black and white drawings, based on their historical and social background, and has suggested black and white expression methods as an aesthetic value consistent with the traditional oriental beauty and emotional sensitivity. This paper is also aimed at studying practical usage of the expression of black and white drawings shown in the TV-CM design, and expanding the scope of design by analyzing the conception, features, and classification of the black and white drawings and searching for a new technique or an oriental expression method.

• Journal of The Korean Association of Information Education
• /
• v.24 no.3
• /
• pp.243-254
• /
• 2020

In order to cultivate AI(artificial intelligence) manpower, major countries are making efforts to apply AI education from elementary school. In order to introduce AI education in elementary school, it is necessary to have a curriculum and educational content for elementary school level. This study developed educational contents to experience the principle of AI learning at the unplugged level for the purpose of AI education for elementary school students. The educational content developed was selected as an AI that evaluates the emotion of sentences. In addition, to solve the problem, data attributes were derived and collected, and the process of AI learning was simulated to solve the problem. As a result of the study, the attitude of elementary school students to AI increased post than before. In addition, the task performance rate was averaged at 85%, showing that the proposed AI education content has educational significance.

• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.667-674
• /
• 2007

Facial expressions provide significant clues about one's emotional state; however, it always has been a great challenge for machine to recognize facial expressions effectively and reliably. In this paper, we report a method of feature-based adaptive motion energy analysis for recognizing facial expression. Our method optimizes the information gain heuristics of ID3 tree and introduces new approaches on (1) facial feature representation, (2) facial feature extraction, and (3) facial feature classification. We use minimal reasonable facial features, suggested by the information gain heuristics of ID3 tree, to represent the geometric face model. For the feature extraction, our method proceeds as follows. Features are first detected and then carefully "selected." Feature "selection" is finding the features with high variability for differentiating features with high variability from the ones with low variability, to effectively estimate the feature's motion pattern. For each facial feature, motion analysis is performed adaptively. That is, each facial feature's motion pattern (from the neutral face to the expressed face) is estimated based on its variability. After the feature extraction is done, the facial expression is classified using the ID3 tree (which is built from the 1728 possible facial expressions) and the test images from the JAFFE database. The proposed method excels and overcomes the problems aroused by previous methods. First of all, it is simple but effective. Our method effectively and reliably estimates the expressive facial features by differentiating features with high variability from the ones with low variability. Second, it is fast by avoiding complicated or time-consuming computations. Rather, it exploits few selected expressive features' motion energy values (acquired from intensity-based threshold). Lastly, our method gives reliable recognition rates with overall recognition rate of 77%. The effectiveness of the proposed method will be demonstrated from the experimental results.

• Korean Journal of Adult Nursing
• /
• v.14 no.4
• /
• pp.543-553
• /
• 2002

Purpose: The purpose of the study was to compare symptoms, medical therapies, and nursing interventions with terminal cancer patients during the last four weeks of their lives in a hospice unit and general units. Method: For the descriptive survey study, data were collected by reviewing the medical records of 243 patients who died of terminal cancer at K hospital in Seoul. The data was analyzed by using Chi-square test and t-test. Result: The study findings are summarized as follows: There were higher frequencies in physical symptoms of constipation, itching sensation, pain, sleeping disturbance, soreness and dysuria for those patients in the hospice unit than those patient in general units. All emotional symptoms were recorded significantly higher for those patients in the hospice unit than those in general units. Regarding the major medical interventions, pain management was used more significantly for those patients in the hospice unit, but antibiotic therapy and resuscitation were used more significantly for those patients in general units. Conclusion: The hospice unit provided more comprehensive nursing interventions including psychological, spiritual, and family cares as well as physiological care for terminal cancer patients. The facts showed that those patients who would need hospice care in general units should be referred to the hospice unit at an appropriate time.