• Journal of Korean Society for Atmospheric Environment
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• v.22 no.4
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• pp.421-430
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• 2006

This paper explores a new methodology of socio-statistical survey to classify environmental perception characteristics and to quantify atmospheric environmental sensitivity of neighboring people around a large industrial complex. In order to compensate intrinsic inclination against environmental problems, Atmospheric Environmental Sensitivity Index (AESI) is proposed as the weighted-summation of four representative questions asking the current status of the local air quality, which are chosen by the factor analysis of questionnaire. Atmospheric environmental perception is tried to be classified into interest/indifference characteristics and rational/emotional perception on environmental issues, positive/negative opinion on the solution of environmental problems. According to the chi-square cross-correlation and two-way layout analyses, it was clearly shown that environmental perception is categorized into two major groups, i.e., the positive-rational group having lower AESI and the negative-emotional group having higher AESI which means more seriously senses the status of local air quality.

• Journal of Korean Academy of Nursing
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• v.39 no.1
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• pp.84-94
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• 2009

Purpose: This study was done to investigate community attitudes towards people with mental illness and to mental health services. Methods: From August to October of 2006, 474 citizens of Siheung city were asked to give demographic data and their personal attitude toward mental illness and mental health services. Results: The residents of Siheung community showed slightly negative attitudes, with a mean opinion about mental illness score of $93.38{\pm}17.29$. According to the study, health professionals and citizens showed a positive attitude to the establishment of day care centers and residential facilities (62.2% and 55.4% respectively). Civil servants showed negative attitudes (40.4%). There were significant differences in attitudes according to gender, age, education level, occupation, religion, and income. Positive attitudes were found for health professionals and negative attitudes for civil servants. Conclusion: The results of this study indicate a need to provide public information and education for civil servants, and for those who have negative attitudes, in particular, men, people in the younger age group, who have lower education levels, who are Buddhists, monthly renters, who have a monthly income below 1 million won, who have no children and no experience with people who have a mental illness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.626-630
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• 2019

After several significant nuclear accidents, public attitudes toward nuclear energy technologies and facilities are considered to be one of the essential factors in the national energy and electricity policy-making process of several nations that employ nuclear energy as their key energy resource. However, it is difficult to explore and capture such an attitude, because the majority of prior studies analyzed public attitudes with a limited number of respondents and fragmentary opinion polls. In order to supplement this point, this study suggests a big data analyzing method with K-LIWC (Korean-Linguistic Inquiry and Word Count), sentiment and query analysis methods, and investigates public attitudes, positive and negative emotional statements about nuclear energy with the collected data sets of well-known social media and network services in Korea over time. Results show that several events and accidents related to nuclear energy have consistent or temporary effects on the attitude and ratios of the statements, depending on the kind of events and accidents. The presented methodology and the use of big data in relation to the energy industry is suggested as it can be helpful in addressing and exploring public attitudes. Based on the results, implications, limitations, and future research areas are presented.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.189-200
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• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.13-30
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• 2023

Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people's perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people's reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used-support vector machine (SVM), naïve Bayes (NB), logistic regression (LOGR), and random forest (RF)-in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.

• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.253-262
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• 2014

This, an association research for design-improvement and model-development of exhibition shops at rural tourism communities, is to secure objective data by analyzing customers' awareness-tendency of and demand for agricultural-specialty exhibition shops. Survey-questions for finding out consumers' awareness-tendency and demand were determined through brainstorming of a professional council, 30 rural communities of which visit-rate by consumers is considerably high were selected for the recruit of 200 consumers. For investigation and analysis, survey and in-depth interview were carried out at the scene with the application of frequency analysis and summarization of their opinions, which revealed that they have a strong will to visit the rural tourism communities for the purchase of agricultural specialties along with the experience of learning-program and on-the-scene direct dealing and that their viewpoint on the direct dealing at the scene was very positive. Also it was confirmed hat their satisfaction with the purchase of agricultural specialties by on-the-scene direct dealing, their pleasure at the purchase, their satisfaction with services and their intention for re-purchase of them were very high while their satisfaction with the exhibition shops was very low. With on-the-scene survey, the consumers' opinions could be listened to in depth. Almost all of them said their satisfaction with the trip to those rural tourism communities was considerably high since they could go to those communities themselves to relieve the stress from their modern life, to experience healing and to see the goods on the scene. Their satisfaction also was attributed to the fact that they have enough trust in purchase along with feeling the warm-heartedness of rural residents. As to their awareness of exhibition shops, they showed a positive response to the on-the-scene direct dealing at rural communities while they, thinking that the space in those exhibition shops was not sufficiently wide, demanded for more systematic counters in more accessible and affordable exhibition shops so that they might be more satisfied with the exhibition shops. Their demand for the necessity of exhibition shops selling agricultural specialties was found to be over 80%, which indicates that the necessity is very high. As to the suitability of function, they have the opinion that the business at those shops had better be focused on sales since they have the understanding of information when they take a trip to the rural communities, while there was another opinion: since agricultural products are seasonal items they should be exhibited and sold at the same time. More than 90% of the respondents had a positive viewpoint on direct dealing of agricultural specialties on the scene, which showed that their response to it was very high. They preferred the permanent shops equipped with roll-around table-booths. In addition, it was revealed that they want systematic exhibition shops in rural communities because they frequent those communities for on-the-scene direct purchase. The preferred type and opinion resulting from estimation of consumers' demands have been reflected for development of practical designs. The structure of variable principles has been designed so that the types of display-case and table-booth might be created. The result of this study is a positive data as a design model which can be utilized at rural communities and will be commercialized for the verification of its validity.

• Fashion & Textile Research Journal
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• v.19 no.2
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• pp.166-179
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• 2017

Fashion industry have been emphasizing on eco-friendly business to enhance their public image. Due to the lack of consumers' awareness and experience of eco fashion advertising, this have resulted in adverse outcomes. Therefore, it is required to develop eco fashion advertisement that meets the public interest of Koreans. This study aims to obtain practical implications which can be applied to further eco fashion advertising. The study examines the public opinion towards eco fashion using Twitter as big data analysis and the protracted implication was provided to consumers as consumer vocabulary to see the advertising effect of consumer vocabulary. In addition, this study focuses on the environmental claim types to identify the most effective advertisement in eco fashion. The results are as follow. Associative claim types had a more positive influence on advertising attitude than substantive claim types. Substantive claim types had a more positive influence on brand cognition than associative claim types. In addition, the moderating effects of consumer vocabulary on advertising attitude and brand cognition were supported in substantive claim types. Advertisement attitude shows positive effects to both brand cognition and brand attitude. It has been proved that brand cognition leads to positive influence towards brand attitude and brand attitude eventually increases consumers' urge to buy products. This study has implication when providing a guideline for eco fashion advertisements.

• Journal of Korean Academy of Nursing
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• v.11 no.1
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• pp.7-17
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• 1981

The purpose of this study is to investigate the relationship between family attitude about mental illness and their general characteristics. The subjects for this study were a sample of 120 families selected from psychiatric ward of one university hospital, which is one national hospital in Seoul. Data was collected from July 1th to August 10th in 3980 used by Opinion about Mental Illness Scale (O.M.I.). The materials were analized by S.P.S.S. program. The findings of the study were as follows: A. Families' attitude toward mental illness shown ay this study was more negative compared to those of Korea1 nursing professorss, nurses and nursing students. B. Variables which influence families' attitude about mental illness: 1. There is no significant between general characteristics and authoritarianism. (p＞ 0.05) 2. Benevolence (Factor B) was found to be significantly related to such variables as religion, eucation levels, existence of mental patient in their an intimate friends. (P < 0.01) Families' attitude about benevolence was mere positive in families who have not relegion or having christion beliefs: haying the higher education levels; not having a mental patient in their an intimate friends. 3. Mental health ideology (Factor C) was found to be significantly related to variable experience of mental illness. (P < 0.01). families' attitude about mental health ideology was more positive in families who had experience of mental illness. 4. Social Restrictiveness (Factor D) was found to be significantly related to variable relationship between families and patients(P<0.01). An intimate friend's attitude about mental health Ideology was mon positive than that of parent and couple. 5. Interpersonal Etiology (Factor E) was found to be significantly related to variable religion (P < 0.05). Families' attitude about interpersonal etiology was more positive in families who have relegion.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.