• Journal of Nutrition and Health
• /
• v.57 no.4
• /
• pp.451-467
• /
• 2024

Purpose: This study aimed to develop a four-hour food additive education program for elementary school students to provide them with accurate information on food additives. Methods: A survey was conducted among 133 elementary school students living in Gyeonggi Province to identify the level of food additive awareness. A four-hour food additive education program and educational materials (PPT, activity sheets, and teacher guidelines) were developed based on the results of the food additive awareness survey. The developed educational programs were based on the Theoretical Model of Stages of Behavior Change. An elementary school nutrition teacher conducted a pilot education for 83 elementary school students to evaluate the feasibility of the developed education program. A survey was conducted to evaluate the effectiveness and satisfaction of the pilot education program. Results: The results of the Food Additive Awareness Survey showed that only 42.1% of people were aware of food additives; 46.3% wanted to know more about food additives, and 54.3% required food additive education. Food coloring (44.7%) and artificial sweeteners (18.7%) were the most common food additives of interest. What they wanted to know about food additives was the safety of food additives (36.8%) and the role and function of food additives (20.3%). After the pilot training on food additives, the level of awareness of food additives was improved significantly, and the percentage of participants who recognized the need for food additive education and promotion increased. According to the satisfaction survey of the food additives education, the interest, understanding, real-life application, learning method, and content amount were approximately 90%. Conclusion: The educational program developed through this study will change the negative perceptions of food additives in elementary school students to a positive one. It will do so by helping nutrition educators educate students on this important subject.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.1
• /
• pp.49-58
• /
• 2010

Corporate social responsibility has become a very important issue for researchers (Greenfield, 2004; Maignan & Ralston, 2002; McWilliams et al., 2006; Pearce & Doh 2005), and many consider it necessary for businesses to define their role in society and apply social and ethical standards to their businesses (Lichtenstein et al., 2004). As a result, a significant number of retailers have adopted CSR as a strategic tool to promote their businesses. To this end, this study sought to discover U.S. consumers' attitudes and behavior in ethical purchasing and consumption based on their subjective perception and evaluation of a retailer. The objectives of this study include: 1) determine the participants awareness of retailers corporate social responsibility; 2) assess how participants evaluate retailers corporate social responsibility; 3) examine whether participants evaluation process of retailers CSR influence their attitude toward the retailer; and 4) assess if participants attitude toward the retailers CSR influence their purchase behavior. This study does not focus on actual retailers' CSR performance because a consumer's decision making process is based on an individual assessment not an actual fact. This study examines US college students' awareness and evaluations of retailers' corporate social responsibility (CSR). Fifty six college students at a major Southeastern university participated in the study. The age of the participants ranged from 18 to 26 years old. Content analysis was conducted with open coding and focused coding. Over 100 single-spaced pages of written responses were collected and analyzed. Two steps of coding (i.e., open coding and focused coding) were conducted (Esterberg, 2002). Coding results and analytic memos were used to understand participants' awareness of CSR and their ethical purchasing behavior supported through the selection and inclusion of direct quotes that were extracted from the written responses. Names used here are pseudonyms to protect confidentiality of participants. Participants were asked to write about retailers, their aware-ness of CSR issues, and to evaluate a retailer's CSR performance. A majority (n = 28) of respondents indicated their awareness of CSR but have not felt the need to act on this issue. Few (n=8) indicated that they are aware of this issue but not greatly concerned. Findings suggest that when college students evaluate retailers' CSR performance, they use three dimensions of CSR: employee support, community support, and environmental support. Employee treatment and support were found as an important criterion in evaluation of retailers' CSR. Respondents indicated that their good experience with a retailer as an employee made them have a positive perception and attitude toward the retailer. Regarding employee support four themes emerged: employee rewards and incentives based on performance, working environment, employee education and training program, and employee and family discounts. Well organized rewards and incentives were mentioned as an important attribute. The factors related to the working environment included: how well retailers follow the rules related to working hours, lunch time and breaks was also one of the most mentioned attributes. Regarding community support, three themes emerged: contributing a percentage of sales to the local community, financial contribution to charity organizations, and events for community support. Regarding environments, two themes emerged: recycling and selling organic or green products. It was mentioned in the responses that retailers are trying to do what they can to be environmentally friendly. One respondent mentioned that the company is creating stores that have an environmentally friendly design. Information about what the company does to help the environment can easily be found on the company’s website as well. Respondents have also noticed that the stores are starting to offer products that are organic and environmentally friendly. A retailer was also mentioned by a respondent in this category in reference to how the company uses eco-friendly cups and how they are helping to rebuild homes in New Orleans. The respondents noticed that a retailer offers reusable bags for their consumers to purchase. One respondent stated that a retailer uses its products to help the environment, through offering organic cotton. After thorough analysis of responses, we found that a participant's evaluation of a retailers' CSR influenced their attitudes towards retailers. However, there was a significant gap between attitudes and purchasing behavior. Although the participants had positive attitudes toward retailers CSR, the lack of funds and time influenced their purchase behavior. Overall, half (n=28) of the respondents mentioned that CSR performance affects their purchasing decisions making when shopping. Findings from this study provide support for retailers to consider their corporate social responsibility when developing their image with the consumer. This study implied that consumers evaluate retailers based on employee, community and environmental support. The evaluation, attitude and purchase behavior of consumers seem to be intertwined. That is, evaluation is based on the knowledge the consumer has of the retailers CSR. That knowledge may influence their attitude toward the retailer and thus influence their purchase behavior. Participants also indicated that having CSR makes them think highly of the retailer, but it does not influence their purchase behavior. Price and convenience seem to surpass the importance of CSR among the participants. Implications, recommendations for future research, and limitations of the study are also discussed.

• The Korean Nurse
• /
• v.28 no.3
• /
• pp.52-67
• /
• 1989

True nursing care means total nursing care which includes physical, emotional and spiritual care. The modern nursing care has tendency to focus toward physical care and needs attention toward emotional and spiritual care. The total nursing care is mandatory for patients with terminal cancer and for this purpose, hospice care became emerged. Hospice case originated from the place or shelter for the travellers to Jerusalem in medieval stage. However, the meaning of modem hospice care became changed to total nursing care for dying patients. Modern hospice care has been developed in England, and spreaded to U.S.A. and Canada for the patients with terminal cancer. Nowaday, it became a part of nursing care and the concept of hospice care extended to the palliative care of the cancer patients. Recently, it was introduced to Korea and received attention as model of total nursing care. This study was attempted to assess the efficacy of hospice care. The purpose of this study was to prove a difference in terms of physical, emotional a d spiritual aspect between the group who received hospice care and who didn't receive hospice care. The subject for this study were 113 patients with advanced cancer who were hospitalized in the S different hospitals. 67 patients received hospice care in 4 different hospitals, and 46 patients didn't receive hospice care in another 4 different hospitals. The method of this study was the questionaire which was made through the descriptive study. The descriptive study was made by individual contact with 102 patients cf advanced cancer for 9 months period. The measurement tool for questionaire was made by author through the descriptive study, and included the personal religious orientation obtained from chung(originated R. Fleck) and 5 emotional stages before dying from Kubler Ross. The content ol questionaire consisted in 67 items which included 11 for general characteristics, 10 for related condition with cancer, 13 for wishes far physical therapy, 13 for emotional reactions and 20 for personal religious orientation. Data for this study was collected from Aug. 25 to Oct. 6 by author and 4 other nurse's who received education and training by author for the collection of data. The collected data were ana lysed using descriptive statistics, $X^2-test$, t-test and pearson correlation coefficient. Results of the study were as follows: "H.C Group" means the group of patient with cancer who received hospice care. "Non H.C Group" means the group of patient with cancer who did not receive hospice care. 1. There is a difference between H.C Group and Non H.C Group in term of the number of physical symptoms, subjective degree of pain sensation and pain control, subjective beliefs in physical cure, emotional reaction, help of present emotional and spiritual care from other personal, needs of emotional and spiritual care in future, selection of treatment method by patients and personal religious orientation. 2. The comparison of H.C Group and Non H.C Group 1) There is no difference in wishes for physical therapy between two groups(p=.522). Among Non H.C Group, a group, who didn't receive traditional therapy and herb medicine was higher than a group who received these in degree of belief that the traditional therapy and herb medicine can cure their disease, and this result was higher in comparison to H.C Group(p=.025, p=.050). 2) Non H.C Group was higher than H.C Group in degree of emotional reaction(p=.050). H.C Group was higher than Non H.C Group in denial and acceptant stage among 5 different emotional stages before dying described by Kubler Ross, especially among the patient who had disease more than 13 months(p=.0069, p=.0198). 3) Non H.C Group was higher than H. C Group in demanding more emotional and spiritual care to doctor, nurse, family and pastor(p=. 010). 4) Non H.C Group was higher than H.C Group in demanding more emotional and spiritual care to each individual of doctor, nurse and family (p=.0110, p=.0029, P=. 0053). 5) H.C Group was higher th2.n Non H.C Group in degree of intrinsic behavior orientation and intrinsic belief orientation of personal religious orientation(p=.034, p=.026). 6) In H.C Group and Non H.C Group, the degree of emotional demanding of christians was significantly higher than non christians to doctor, nurse, family and pastor(p=. 000, p=.035). 7) In H.C Group there were significant positive correlations as following; (1) Between the degree of emotional demandings to doctor, nurse, family & pastor and: the degree of intrinsic behavior orientation in personal religious orientation(r=. 5512, p=.000). (2) Between the degree of emotional demandings to doctor, nurse. family & pastor and the degree of intrinsic belief orientation in personal religious orientation(r=.4795, p=.000). (3) Between the degree of intrinsic behavior orientation and the degree of intrinsic: belief orientation in personal religious orientation(r=.8986, p=.000). (4) Between the degree of extrinsic religious orientation and the degree of consensus religious orientation in personal religious orientation (r=. 2640, p=.015). In H.C. Group there were significant negative correlations as following; (1) Between the degree of intrinsic behavior orientation and extrinsic religious orientation in personal religious orientation (r=-.4218, p=.000). (2) Between the degree or intrinsic behavior orientation and consensus religious orientation in personal religious orientation(r=-. 4597, p=.000). (3) Between the degree of intrinsic belief orientations and the degree of extrinsic religious orientation in personal religious orientation(r=-.4388, p=.000). (4) Between the degree of intrinsic belief orientation and the degree of consensus religious orientation in personal religious orientation(r=-. 5424, p=.000). 8) In Non H.C Group there were significant positive correlation as following; (1) Between the degree of emotional demandings to doctor, nurse, family & pastor and the degree of intrinsic behavior orientation in personal religious orientation(r= .3566, p=.007). (2) Between the degree of emotional demandings to doctor, nurse, family & pastor and the degree of intrinsic belief orientation in personal religious orientation(r=.3430, p=.010). (3) Between the degree of intrinsic behavior orientation and the degree of intrinsic belief orientation in personal religious orientation(r=.9723, p=.000). In Non H.C Group there were significant negative correlation as following; (1) Between the degree of emotional demandings to doctor, nurse, family & pastor and the degree of extrinsic religious orientation in personal religious orientation(r= -.2862, p=.027). (2) Between the degree of intrinsic behavior orientation and the degree of extrinsic religious orientation in personal religious orientation(r=-. 5083, p=.000). (3) Between the degree of intrinsic belief orientation and the degree of extrinsic religious orientation in personal religious orientation(r=-. 5013, p=.000). In conclusion above datas suggest that hospice care provide effective total nursing care for the patients with terminal cancer, and hospice care is mandatory in all medical institutions.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.141-154
• /
• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

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