• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5927-5936
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• 2014

Introduction: Breast cancer is the most common cancer among Jordanians. Breast cancer patients suffer from several negative consequences after treatment and these include pain, fatigue, sexual problems, appearance and body image concerns, with psychological dysfunction. This could affect the patient quality of life and psychological well-being. To the best of our knowledge, there is no published quantitative data on the quality of life and psychological well-being of breast cancer patients in Jordan. The objective of this study was to obtain such data and assess predictors with calculated scores. Methods: In this cross-sectional study conducted among breast cancer patients in Jordan diagnosed in 2009 and 2010, assessment was performed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), the Breast Module (QLQ-BR23) and the Hospital Anxiety and Depression Scale (HADS). Clinical, demographic and psychosocial indicators that could predict patient quality of life scores were collected. Results: The number of patients interviewed was 236 (mean age=$50.7{\pm}10.7$ years). The mean Global Health score for the QLQ-C30 was $63.7{\pm}20.2$ SD. Among functional scales, "social functioning" scored the highest ($mean=78.1{\pm}28.6$ SD), whereas "emotional functioning" scored the lowest ($mean=59.0{\pm}SD\;33.5$). For the QLQ-BR23, the worst scores within the functional scales were for "body image" ($mean=52.1{\pm}36.8$ SD) and "future perspective" ($mean=52.9{\pm}38.5$ SD). The worst symptom was "upset by hair loss" ($mean=69.8{\pm}43.0$). The mean HADS scores was $18.{\pm}9.0$ SD. Out of study participants, 53% scored abnormal on the anxiety scale and 45% on the depression scale. Severe depression and severe anxiety were detected among 8% and 14% of study participants, respectively. Statistically significant predictors for individual scores were similar to those reported in published studies, such as the presence of recurrence since baseline, family history of cancer, low educational status, current social problems, extent of the disease, presence of financial difficulties, and employment status. Conclusions and Recommendations: Breast cancer survivors in Jordan have overall good quality of life scores when compared with patients from Western countries. However, their psychological wellbeing is more impaired. There is an urgent need for psychosocial support programs and psychological screening and consultation for breast cancer patients at hospitals of the Ministry of Health in Jordan.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.518-524
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• 2020

This study conducted basic research on an LED lighting design to improve the learning effect from brain wave analysis. The ideal environments of mathematics, language, and creative region can be different. Inside the space where the lighting environment can be experienced directly, the test subject consisted of common elements. Other lighting was blocked completely in controlled lighting conditions. The brain waves were analyzed according to the change in color temperature and illumination. The analyzer used was fabricated by EMOTIV Company. In the variable RGB LED light, the color of the light was measured, and the brain wave of each subject was determined. LED lights have variable color temperature (3000 [K], 4500 [K]. 250 [lux], 70% -350 [lux], 100% -500 [lux]). As research results, the highest concentration in a mathematics study was in the general condition of a high color temperature, in which the optimal condition was a 6000[K] color temperature and 350[lux] illumination. The optimal condition for a language study was a 4500[K] color temperature and 500[lux] illumination, and that of the creative study was 3000[K] color temperature and 500[lux] illumination. Overall, the possibility of emotional ability and concentrated learning efficiency can be improved by the LED lighting design with the color temperature and illumination.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.3-16
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• 2018

The emergence of the fourth industrial revolution is rapidly changing the conventional society and the industry, eroding the boundaries among the technology, culture, and finance. In the mobility industry, as the engineering-based industry converges with the information technology, the mobile space is changing from mobility or safety-centric space into space where the passengers can consume infotainment or contents services. The contents evaluation of the future mobility is conducted in terms of usability or technology acceptance aspect, but according to the trend analysis, the mobility industries, such as vehicle OEMs, it is necessary to evaluate the emotional or sensibility factors for the development of their future mobile space design. Herein, this research study evaluates which sensibility factor should be evaluated in priority to develop the contents interaction in the future mobile space. Thus, using Patrick Jordan's Four Pleasure Model, the priority evaluation has been conducted among 116 Korean drivers. As a result of the statistical analysis and AHP (Analytic Hierarchy Process), it has been found that first, it is necessary to evaluate psychological, ideological, social and physical sensibility in the respective order, and second, it is necessary to evaluate based on the contents user type.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.122-131
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• 2016

In the smart societies, such as the current unlike in the past, the voice that listeners will feel favor is changing through the development of ICT technologies and infrastructure. In other words, in the past, loud, intensive and fast voice is a favorite but now a new social and cultural situation that is changing them with ICT technologies. Now, this becomes one of the important things that we clarify 'Is it a voice that feels a favor?'. For this, in this paper, we identified what voice that listeners feel favor by applying ICT technologies. Studies were carried out to proceed largely divided into two categories. Firstly, as the quantified data, we extracted the impact on favorable feeling of listeners which related with emotional speech by empirical analysis work. To do this, we performed the experiment for the public. Secondly, we identified what kind of voice which listeners feel a good impression. For this, we identified voice characteristics that there are people who are influential in the real society. Also, we extracted both the voice characteristics of each influential people and common voice characteristics. In addition, we want to overcome the problems of qualitative methods that have originally limitations in objective respects which is significant to the voice analysis. For this, we performed the experiments of the voice analysis by numerical and visual approaches.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.65-74
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• 2021

In this study, we further study the customer's positive emotion about the impact of different inherent service climate on the emotions and satisfaction of the customers who receive the service. Through this, the purpose was to present the direction of creating a service climate. As a research method, structural equation statistical analysis, such as measurement model analysis and structural model analysis, was performed using SmartPLS (v.3.2) for data collected in surveys. Looking at the research results, first, a company's service climate has a positive (+) impact on positive customer emotions: pleasure, pleasure, and happiness. This can be interpreted as an indication that creating a business climate for service is an important factor that elicits positive emotions from customers. Second, a company's service climate and positive customer emotion also have a positive impact on customer satisfaction. Finally, when a company's service climate affects customer satisfaction, happiness has the greatest mediating effect among several parameters. This demonstrated empirically that satisfying the happy feelings of customers is the most important of the company's service climate. Since this study is aimed at a small number of restaurant companies, there is a limit to generalizing the findings and applying them to all restaurant companies. Nevertheless, it is meaningful to study the emotions of positive customers when the service climate affects customer satisfaction, and we hope that the company's analysis of service climate will continue to improve customer satisfaction through various emotional analysis as well as positive factors.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Journal of Naturopathy
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• v.9 no.2
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• pp.46-56
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• 2020

Purpose: This study proposes a concert oriented music therapy (COMT) program which takes into account the personal and social factors of depression, and its clinical effects were validated on elderly persons with visual impairments who were susceptible to depression. The experiment was conducted on 34 elderly persons with visual impairments, randomly allocated to either the experimental group (n = 15) or the control group (n = 19). The COMT program was applied 20 times in total to the experimental group for 110 minutes every other week, for a duration of 10 months. The depression level was evaluated using the Korean form of the Geriatric Depression Scale (KGDS). The experimental results showed that the average KGDS score in the experimental group decreased by 4.1 points (19.4%), from 21.0 to 16.9, whereas it increased by 1.16 points (5.8%), from 19.95 to 21.11, in the control group. The reduction in the depression level was significant for the following 4 depression factors : decreased social interest (40%), negative thinking and the unhappiness feeling (20%), emotional discomfort (16.3%), and cognitive dysfunction (7.6%), whereas it was negligible in the factors of physical weakening and decreased vitality. The COMT program reduced the KGDS scores of the experimental group regardless of their experience in musical performance. In conclusion, the proposed COMT program proved to be efficacious in alleviating depression in elderly persons with visual impairments. In particular, it proved highly effective in helping with decreased social interest, namely, the social factor of depression which is not properly considered in existing music therapy methods.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.271-281
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• 2019

The purpose of this study is to provide basic data for improving the adaptability of college life by examining the effects of individuality and relationship of university freshmen on college life adaptation. The study subjects were 383 freshmen enrolled in a university in Chungbuk C City, and a multiple regression analysis was conducted to examine the factors of impact. The first study found that boys were more individual in genders, depending on the general characteristics. Extroverted students were more relational. In the majors, students in the humanities and social sciences were more related, and students in the natural engineering department were more individual. Second, the lower factors affecting college students' adaptation to college life were found to be autonomous in individuality, and affinity and intimacy in relation. In particular, autonomy has the greatest impact on adaptation to college life, followed by affinity and intimacy. Based on these results, policy suggestions are needed first, it is necessary to balance and balance individuality and relationship. second, it is necessary to create activities and learning environments that you can choose for yourself. third, it is necessary to develop programs to promote affinity and intimacy such as department events and club activities. fourth, emotional and psychological program support through face-to-face contact should be activated to improve individuality and relationship.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.266-269
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• 2022

As the emotional problems experienced by modern people after COVID-19 have increased, the burden on clients due to negative perceptions of psychological counseling and physical limitations is still high. Therefore, this study proposes a virtual counseling platform for effective counseling using the characteristics of people in their 20s who are open to relationships in non-face-to-face relationships. To this end, a survey was conducted for those in their 20s and in-depth interviews with counselors were conducted. After that, a 3D virtual counseling environment was created through Unity to PC-based service. As a result of the study, it was found that the most important factor in counseling in the 20s was the tendency of counselors and an atmosphere without burden, which was closely related to self-opening in counseling. Therefore, it was possible to participate in counseling while looking at the counselor character in a 3D space created in comfortable atmosphere, and various interaction elements with the counselor were provided during counseling. The significance of this study aims to promote self-openness of clients in their 20s and actively participate in counseling. This is expected to greatly help improve psychological problems through smooth counseling.

• 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.